{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> 作者：Emmanuelle Gouillart、Didrik Pinte、Gaël Varoquaux 和 Pauli Virtanen\n",
    "\n",
    "本章给出关于Numpy概述，Numpy是Python中高效数值计算的核心工具。\n",
    "\n",
    "## 1.3.1 Numpy 数组对象\n",
    "\n",
    "### 1.3.1.1 什么是Numpy以及Numpy数组？\n",
    "\n",
    "#### 1.3.1.1.1 Numpy数组\n",
    "\n",
    "**Python对象：**\n",
    "\n",
    "* 高级数值对象：整数、浮点\n",
    "* 容器：列表（无成本插入和附加），字典（快速查找）\n",
    "\n",
    "**Numpy提供：**\n",
    "\n",
    "* 对于多维度数组的Python扩展包\n",
    "* 更贴近硬件（高效）\n",
    "* 为科学计算设计（方便）\n",
    "* 也称为*面向数组计算*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 1, 2, 3])"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "\n",
    "a = np.array([0, 1, 2, 3])\n",
    "a"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "例如，数组包含：\n",
    "\n",
    "* 实验或模拟在离散时间阶段的值\n",
    "* 测量设备记录的信号，比如声波\n",
    "* 图像的像素、灰度或颜色\n",
    "* 用不同X-Y-Z位置测量的3-D数据，例如MRI扫描\n",
    "\n",
    "...\n",
    "\n",
    "**为什么有用：**提供了高速数值操作的节省内存的容器。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "344 µs ± 41.8 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)\n"
     ]
    }
   ],
   "source": [
    "L = range(1000)\n",
    "%timeit [i**2 for i in L]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3.28 µs ± 569 ns per loop (mean ± std. dev. of 7 runs, 100000 loops each)\n"
     ]
    }
   ],
   "source": [
    "a = np.arange(1000)\n",
    "# print(a)\n",
    "%timeit a**2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 1.3.1.1.2 Numpy参考文档\n",
    "\n",
    "* 线上: http://docs.scipy.org/\n",
    "* 交互帮助:\n",
    "\n",
    "```python\n",
    "np.array?\n",
    "String Form:<built-in function array>\n",
    "Docstring:\n",
    "array(object, dtype=None, copy=True, order=None, subok=False, ndmin=0, ...\n",
    "```\n",
    "\n",
    "查找东西："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Search results for 'create array'\n",
      "---------------------------------\n",
      "numpy.array\n",
      "    Create an array.\n",
      "numpy.memmap\n",
      "    Create a memory-map to an array stored in a *binary* file on disk.\n",
      "numpy.diagflat\n",
      "    Create a two-dimensional array with the flattened input as a diagonal.\n",
      "numpy.fromiter\n",
      "    Create a new 1-dimensional array from an iterable object.\n",
      "numpy.partition\n",
      "    Return a partitioned copy of an array.\n",
      "numpy.ma.diagflat\n",
      "    Create a two-dimensional array with the flattened input as a diagonal.\n",
      "numpy.ctypeslib.as_array\n",
      "    Create a numpy array from a ctypes array or a ctypes POINTER.\n",
      "numpy.ma.make_mask\n",
      "    Create a boolean mask from an array.\n",
      "numpy.ctypeslib.as_ctypes\n",
      "    Create and return a ctypes object from a numpy array.  Actually\n",
      "numpy.ma.mrecords.fromarrays\n",
      "    Creates a mrecarray from a (flat) list of masked arrays.\n",
      "numpy.lib.format.open_memmap\n",
      "    Open a .npy file as a memory-mapped array.\n",
      "numpy.ma.MaskedArray.__new__\n",
      "    Create a new masked array from scratch.\n",
      "numpy.lib.arrayterator.Arrayterator\n",
      "    Buffered iterator for big arrays.\n",
      "numpy.ma.mrecords.fromtextfile\n",
      "    Creates a mrecarray from data stored in the file `filename`.\n",
      "numpy.asarray\n",
      "    Convert the input to an array.\n",
      "numpy.ndarray\n",
      "    ndarray(shape, dtype=float, buffer=None, offset=0,\n",
      "numpy.recarray\n",
      "    Construct an ndarray that allows field access using attributes.\n",
      "numpy.chararray\n",
      "    chararray(shape, itemsize=1, unicode=False, buffer=None, offset=0,\n",
      "numpy.pad\n",
      "    Pads an array.\n",
      "numpy.sum\n",
      "    Sum of array elements over a given axis.\n",
      "numpy.asanyarray\n",
      "    Convert the input to an ndarray, but pass ndarray subclasses through.\n",
      "numpy.copy\n",
      "    Return an array copy of the given object.\n",
      "numpy.diag\n",
      "    Extract a diagonal or construct a diagonal array.\n",
      "numpy.load\n",
      "    Load arrays or pickled objects from ``.npy``, ``.npz`` or pickled files.\n",
      "numpy.sort\n",
      "    Return a sorted copy of an array.\n",
      "numpy.array_equiv\n",
      "    Returns True if input arrays are shape consistent and all elements equal.\n",
      "numpy.dtype\n",
      "    Create a data type object.\n",
      "numpy.choose\n",
      "    Construct an array from an index array and a set of arrays to choose from.\n",
      "numpy.nditer\n",
      "    Efficient multi-dimensional iterator object to iterate over arrays.\n",
      "numpy.swapaxes\n",
      "    Interchange two axes of an array.\n",
      "numpy.full_like\n",
      "    Return a full array with the same shape and type as a given array.\n",
      "numpy.ones_like\n",
      "    Return an array of ones with the same shape and type as a given array.\n",
      "numpy.empty_like\n",
      "    Return a new array with the same shape and type as a given array.\n",
      "numpy.zeros_like\n",
      "    Return an array of zeros with the same shape and type as a given array.\n",
      "numpy.asarray_chkfinite\n",
      "    Convert the input to an array, checking for NaNs or Infs.\n",
      "numpy.diag_indices\n",
      "    Return the indices to access the main diagonal of an array.\n",
      "numpy.ma.choose\n",
      "    Use an index array to construct a new array from a set of choices.\n",
      "numpy.chararray.tolist\n",
      "    a.tolist()\n",
      "numpy.matlib.rand\n",
      "    Return a matrix of random values with given shape.\n",
      "numpy.savez_compressed\n",
      "    Save several arrays into a single file in compressed ``.npz`` format.\n",
      "numpy.ma.empty_like\n",
      "    Return a new array with the same shape and type as a given array.\n",
      "numpy.ma.make_mask_none\n",
      "    Return a boolean mask of the given shape, filled with False.\n",
      "numpy.ma.mrecords.fromrecords\n",
      "    Creates a MaskedRecords from a list of records.\n",
      "numpy.around\n",
      "    Evenly round to the given number of decimals.\n",
      "numpy.source\n",
      "    Print or write to a file the source code for a Numpy object.\n",
      "numpy.diagonal\n",
      "    Return specified diagonals.\n",
      "numpy.histogram2d\n",
      "    Compute the bi-dimensional histogram of two data samples.\n",
      "numpy.fft.ifft\n",
      "    Compute the one-dimensional inverse discrete Fourier Transform.\n",
      "numpy.fft.ifftn\n",
      "    Compute the N-dimensional inverse discrete Fourier Transform.\n",
      "numpy.busdaycalendar\n",
      "    A business day calendar object that efficiently stores information"
     ]
    }
   ],
   "source": [
    "np.lookfor('create array')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "```python\n",
    "np.con*?\n",
    "np.concatenate\n",
    "np.conj\n",
    "np.conjugate\n",
    "np.convolve\n",
    "```\n",
    "\n",
    "#### 1.3.1.1.3 导入惯例\n",
    "\n",
    "导入numpy的推荐惯例是："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true,
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1.3.1.2 创建数组\n",
    "#### 1.3.1.2.1 手动构建数组\n",
    "\n",
    "* 1-D："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 1, 2, 3])"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a = np.array([0, 1, 2, 3])\n",
    "a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a.ndim"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(4,)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(a)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* 2-D，3-D，...："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0, 1, 2],\n",
       "       [3, 4, 5]])"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "b = np.array([[0, 1, 2], [3, 4, 5]])    # 2 x 3 数组\n",
    "b"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "b.ndim"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2, 3)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "b.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(b)     # 返回一个纬度的大小"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[[1],\n",
       "        [2]],\n",
       "\n",
       "       [[3],\n",
       "        [4]]])"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "c = np.array([[[1], [2]], [[3], [4]]])\n",
    "c"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2, 2, 1)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "c.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**练习：简单数组**\n",
    "\n",
    "* 创建一个简单的二维数组。首先，重复上面的例子。然后接着你自己的：在第一行从后向前数奇数，接着第二行数偶数？\n",
    "* 在这些数组上使用函数[len()](http://docs.python.org/2.7/library/functions.html#len)、numpy.shape()。他们有什么关系？与数组的`ndim`属性间呢？\n",
    "\n",
    "#### 1.3.1.2.2 创建数组的函数\n",
    "\n",
    "实际上，我们很少一项一项地输入...\n",
    "\n",
    "* 均匀分布："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a = np.arange(10) # 0 .. n-1  (!)\n",
    "a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 3, 5, 7])"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "b = np.arange(1, 9, 2) # 开始，结束（不包含），步长\n",
    "b"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* 或者通过一些数据点："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0. , 0.2, 0.4, 0.6, 0.8, 1. ])"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "c = np.linspace(0, 1, 6)   # 起点、终点、数据点\n",
    "c"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0. ,  0.2,  0.4,  0.6,  0.8])"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d = np.linspace(0, 1, 5, endpoint=False)\n",
    "d"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* 普通数组："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 1.,  1.,  1.],\n",
       "       [ 1.,  1.,  1.],\n",
       "       [ 1.,  1.,  1.]])"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a = np.ones((3, 3))  # 提示: (3, 3) 是元组\n",
    "a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.,  0.],\n",
       "       [ 0.,  0.]])"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "b = np.zeros((2, 2))\n",
    "b"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 1.,  0.,  0.],\n",
       "       [ 0.,  1.,  0.],\n",
       "       [ 0.,  0.,  1.]])"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "c = np.eye(3)\n",
    "c"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1, 0, 0, 0],\n",
       "       [0, 2, 0, 0],\n",
       "       [0, 0, 3, 0],\n",
       "       [0, 0, 0, 4]])"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d = np.diag(np.array([1, 2, 3, 4]))\n",
    "d"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* `np.random`: 随机数 (Mersenne Twister PRNG) :"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0.05504731,  0.38154156,  0.39639478,  0.22379146])"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a = np.random.rand(4)       # [0, 1] 的均匀分布\n",
    "a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0.26860008,  0.89988621, -0.5573221 , -1.44907529])"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "b = np.random.randn(4)      # 高斯\n",
    "b"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": true,
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "None\n"
     ]
    }
   ],
   "source": [
    "print(np.random.seed(1234))      # 设置随机种子"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": true,
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7853585837137692"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.random.rand()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**练习：用函数创建数组**\n",
    "* 实验用`arange`、`linspace`、`ones`、`zeros`、`eye`和`diag`。\n",
    "* 用随机数创建不同类型的数组。\n",
    "* 在创建带有随机数的数组前设定种子。\n",
    "* 看一下函数`np.empty`。它能做什么？什么时候会比较有用？"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.  0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9]\n"
     ]
    }
   ],
   "source": [
    "x = np.arange(0, 1, 0.1)\n",
    "print(x)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1.3.1.3基础数据类型"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "你可能已经发现，在一些情况下，数组元素显示带有点（即 2. VS 2）。这是因为所使用的数据类型不同："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dtype('int64')"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a = np.array([1, 2, 3])\n",
    "a.dtype"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dtype('float64')"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "b = np.array([1., 2.0, 3.0])\n",
    "b.dtype"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "不同的数据类型可以更紧凑的在内存中存储数据，但是大多数时候我们都只是操作浮点数据。注意，在上面的例子中，Numpy自动从输入中识别了数据类型。\n",
    "\n",
    "你可以明确的指定想要的类型："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dtype('float64')"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "c = np.array([1, 2, 3], dtype=float)\n",
    "c.dtype"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**默认**数据类型是浮点:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dtype('float64')"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a = np.ones((3, 3))\n",
    "a.dtype"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "其他类型：\n",
    "\n",
    "**复数**："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dtype('complex128')"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d = np.array([1+2j, 3+4j, 5+6*1j])\n",
    "d.dtype"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**布尔**："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dtype('bool')"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "e = np.array([True, False, False, True])\n",
    "e.dtype"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**字符**："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dtype('S7')"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "f = np.array(['Bonjour', 'Hello', 'Hallo',])\n",
    "f.dtype     # <--- 包含最多7个字母的字符"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**更多**：\n",
    "\n",
    "* int32\n",
    "* int64\n",
    "* unit32\n",
    "* unit64"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1.3.1.4基本可视化\n",
    "\n",
    "现在我们有了第一个数组，我们将要进行可视化。\n",
    "\n",
    "从*pylab*模式启动IPython。\n",
    "\n",
    "```\n",
    "ipython --pylab\n",
    "```\n",
    "\n",
    "或notebook：\n",
    "\n",
    "```\n",
    "ipython notebook --pylab=inline\n",
    "```\n",
    "\n",
    "或者如果IPython已经启动，那么："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Using matplotlib backend: MacOSX\n",
      "Populating the interactive namespace from numpy and matplotlib\n"
     ]
    }
   ],
   "source": [
    "%pylab"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "或者从Notebook中："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Populating the interactive namespace from numpy and matplotlib\n"
     ]
    }
   ],
   "source": [
    "%pylab inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`inline` 对notebook来说很重要，以便绘制的图片在notebook中显示而不是在新窗口显示。\n",
    "\n",
    "*Matplotlib*是2D制图包。我们可以像下面这样导入它的方法："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true,
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt  #整洁形式"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "然后使用（注你需要显式的使用 `show` ）:\n",
    "\n",
    "```python\n",
    "plt.plot(x, y)       # 线图\n",
    "plt.show()           # <-- 显示图表（使用pylab的话不需要）\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "或者，如果你使用 `pylab`：\n",
    "\n",
    "```python\n",
    "plt.plot(x, y)       # 线图\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "在脚本中推荐使用 `import matplotlib.pyplot as plt`。 而交互的探索性工作中用 `pylab`。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* 1D作图："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1068f38d0>]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x106846190>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = np.linspace(0, 3, 20)\n",
    "y = np.linspace(0, 9, 20)\n",
    "plt.plot(x, y)       # 线图"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x106b32090>]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1069bfd90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(x, y, 'o')  # 点图"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* 2D 作图:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.colorbar.Colorbar instance at 0x106a095f0>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x106b48150>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "image = np.random.rand(30, 30)\n",
    "plt.imshow(image, cmap=plt.cm.hot)    \n",
    "plt.colorbar()    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "更多请见[matplotlib部分](http://localhost:8889/notebooks/1.4Matplotlib%EF%BC%9A%E7%BB%98%E5%9B%BE.ipynb)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**练习**：简单可视化\n",
    "\n",
    "画出简单的数组：cosine作为时间的一个函数以及2D矩阵。\n",
    "\n",
    "在2D矩阵上试试使用 `gray` colormap。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 1.3.1.5索引和切片"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "数组的项目可以用与其他Python序列（比如：列表）一样的方式访问和赋值："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a = np.arange(10)\n",
    "a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0, 2, 9)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a[0], a[2], a[-1]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**警告**：索引从0开始与其他的Python序列（以及C/C++）一样。相反，在Fortran或者Matlab索引从1开始。\n",
    "\n",
    "使用常用的Python风格来反转一个序列也是支持的："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([9, 8, 7, 6, 5, 4, 3, 2, 1, 0])"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a[::-1]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "对于多维数组，索引是整数的元组："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0, 0, 0],\n",
       "       [0, 1, 0],\n",
       "       [0, 0, 2]])"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a = np.diag(np.arange(3))\n",
    "a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a[1, 1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0,  0,  0],\n",
       "       [ 0,  1,  0],\n",
       "       [ 0, 10,  2]])"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a[2, 1] = 10 # 第三行，第二列\n",
    "a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 1, 0])"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a[1]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**注**：\n",
    "\n",
    "* 在2D数组中，第一个维度对应行，第二个维度对应列。\n",
    "* 对于多维度数组 `a`，a[0]被解释为提取在指定维度的所有元素\n",
    "\n",
    "**切片**：数组与其他Python序列也可以被切片："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a = np.arange(10)\n",
    "a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([2, 5, 8])"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a[2:9:3] # [开始:结束:步长]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "注意最后一个索引是不包含的！："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 1, 2, 3])"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a[:4]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "切片的三个元素都不是必选：默认情况下，起点是0，结束是最后一个，步长是1："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 2])"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a[1:3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 2, 4, 6, 8])"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a[::2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([3, 4, 5, 6, 7, 8, 9])"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a[3:]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Numpy索引和切片的一个小说明...\n",
    "\n",
    "![numpy_indexing](http://scipy-lectures.github.io/_images/numpy_indexing.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "赋值和切片可以结合在一起："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0,  1,  2,  3,  4, 10, 10, 10, 10, 10])"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a = np.arange(10)\n",
    "a[5:] = 10\n",
    "a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 1, 2, 3, 4, 4, 3, 2, 1, 0])"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "b = np.arange(5)\n",
    "a[5:] = b[::-1]\n",
    "a"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**练习：索引与切片**\n",
    "* 试试切片的特色，用起点、结束和步长：从linspace开始，试着从后往前获得奇数，从前往后获得偶数。\n",
    "重现上面示例中的切片。你需要使用下列表达式创建这个数组："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0,  1,  2,  3,  4,  5],\n",
       "       [10, 11, 12, 13, 14, 15],\n",
       "       [20, 21, 22, 23, 24, 25],\n",
       "       [30, 31, 32, 33, 34, 35],\n",
       "       [40, 41, 42, 43, 44, 45],\n",
       "       [50, 51, 52, 53, 54, 55]])"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.arange(6) + np.arange(0, 51, 10)[:, np.newaxis]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**练习：数组创建**\n",
    "\n",
    "创建下列的数组（用正确的数据类型）：\n",
    "\n",
    "```python\n",
    "[[1, 1, 1, 1],\n",
    " [1, 1, 1, 1],\n",
    " [1, 1, 1, 2],\n",
    " [1, 6, 1, 1]]\n",
    "\n",
    "[[0., 0., 0., 0., 0.],\n",
    " [2., 0., 0., 0., 0.],\n",
    " [0., 3., 0., 0., 0.],\n",
    " [0., 0., 4., 0., 0.],\n",
    " [0., 0., 0., 5., 0.],\n",
    " [0., 0., 0., 0., 6.]]\n",
    "```\n",
    "\n",
    "参考标准：每个数组\n",
    "\n",
    "提示：每个数组元素可以像列表一样访问，即a[1] 或 a[1, 2]。\n",
    "\n",
    "提示：看一下 `diag` 的文档字符串。\n",
    "\n",
    "**练习：创建平铺数组**\n",
    "\n",
    "看一下 `np.tile` 的文档，是用这个函数创建这个数组：\n",
    "\n",
    "```python\n",
    "[[4, 3, 4, 3, 4, 3],\n",
    " [2, 1, 2, 1, 2, 1],\n",
    " [4, 3, 4, 3, 4, 3],\n",
    " [2, 1, 2, 1, 2, 1]]\n",
    "```\n",
    "\n",
    "### 1.3.1.6 副本和视图\n",
    "\n",
    "切片操作创建原数组的一个**视图**，这只是访问数组数据一种方式。因此，原始的数组并不是在内存中复制。你可以用 `np.may_share_memory()` 来确认两个数组是否共享相同的内存块。但是请注意，这种方式使用启发式，可能产生漏报。\n",
    "\n",
    "**当修改视图时，原始数据也被修改**："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a = np.arange(10)\n",
    "a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 2, 4, 6, 8])"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "b = a[::2]\n",
    "b"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.may_share_memory(a, b)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([12,  2,  4,  6,  8])"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "b[0] = 12\n",
    "b"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([12,  1,  2,  3,  4,  5,  6,  7,  8,  9])"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a   # (!)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a = np.arange(10)\n",
    "c = a[::2].copy()  # 强制复制\n",
    "c[0] = 12\n",
    "a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.may_share_memory(a, c)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "乍看之下这种行为可能有些奇怪，但是这样做节省了内存和时间。\n",
    "\n",
    "**实例：素数筛选**\n",
    "\n",
    "![prime](http://scipy-lectures.github.io/_images/prime-sieve.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "用筛选法计算0-99之间的素数\n",
    "\n",
    "* 构建一个名为 `_prime` 形状是 (100,) 的布尔数组，在初始将值都设为True："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "collapsed": true,
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [],
   "source": [
    "is_prime = np.ones((100,), dtype=bool)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* 将不属于素数的0，1去掉"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "collapsed": true,
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [],
   "source": [
    "is_prime[:2] = 0"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "对于从2开始的整数 `j` ，化掉它的倍数："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "collapsed": true,
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [],
   "source": [
    "N_max = int(np.sqrt(len(is_prime)))\n",
    "for j in range(2, N_max):\n",
    "    is_prime[2*j::j] = False"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* 看一眼 `help(np.nonzero)`，然后打印素数\n",
    "* 接下来:\n",
    "    * 将上面的代码放入名为 `prime_sieve.py` 的脚本文件\n",
    "    * 运行检查一下时候有效\n",
    "    * 使用[埃拉托斯特尼筛法](http://en.wikipedia.org/wiki/Sieve_of_Eratosthenes)的优化建议\n",
    "        1. 跳过已知不是素数的 `j`\n",
    "        2. 第一个应该被划掉的数是$j^2$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 1.3.1.7象征索引\n",
    "\n",
    "Numpy数组可以用切片索引，也可以用布尔或整形数组（面具）。这个方法也被称为象征索引。它创建一个副本而不是视图。\n",
    "\n",
    "#### 1.3.1.7.1使用布尔面具"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([10,  3,  8,  0, 19, 10, 11,  9, 10,  6,  0, 20, 12,  7, 14])"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.random.seed(3)\n",
    "a = np.random.random_integers(0, 20, 15)\n",
    "a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([False,  True, False,  True, False, False, False,  True, False,\n",
       "        True,  True, False,  True, False, False], dtype=bool)"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(a % 3 == 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 3,  0,  9,  6,  0, 12])"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mask = (a % 3 == 0)\n",
    "extract_from_a = a[mask] # 或,  a[a%3==0]\n",
    "extract_from_a           # 用面具抽取一个子数组"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "赋值给子数组时，用面具索引非常有用："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([10, -1,  8, -1, 19, 10, 11, -1, 10, -1, -1, 20, -1,  7, 14])"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a[a % 3 == 0] = -1\n",
    "a"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 1.3.1.7.2 用整型数组索引"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0, 10, 20, 30, 40, 50, 60, 70, 80, 90])"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a = np.arange(0, 100, 10)\n",
    "a"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "索引可以用整型数组完成，其中相同的索引重复了几次："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([20, 30, 20, 40, 20])"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a[[2, 3, 2, 4, 2]]  # 注：[2, 3, 2, 4, 2] 是Python列表"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "用这种类型的索引可以分配新值："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([   0,   10,   20,   30,   40,   50,   60, -100,   80, -100])"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a[[9, 7]] = -100\n",
    "a"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "当一个新数组用整型数组索引创建时，新数组有相同的形状，而不是整数数组："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2, 2)"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a = np.arange(10)\n",
    "idx = np.array([[3, 4], [9, 7]])\n",
    "idx.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[3, 4],\n",
       "       [9, 7]])"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a[idx]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "下图展示了多种象征索引的应用\n",
    "![numpy_fancy_indexing](http://scipy-lectures.github.io/_images/numpy_fancy_indexing.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**练习：象征索引**\n",
    "* 同样，重新生成上图中所示的象征索引\n",
    "* 用左侧的象征索引和右侧的数组创建在为一个数组赋值，例如，设置上图数组的一部分为0。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1.3.2 数组的数值操作\n",
    "### 1.3.2.1 元素级操作\n",
    "#### 1.3.2.1.1 基础操作\n",
    "\n",
    "标量:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([2, 3, 4, 5])"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a = np.array([1, 2, 3, 4])\n",
    "a + 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 2,  4,  8, 16])"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "2**a"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "所有运算是在元素级别上操作："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-1.,  0.,  1.,  2.])"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "b = np.ones(4) + 1\n",
    "a - b"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 2.,  4.,  6.,  8.])"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a * b"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 2,  3,  6, 13, 28])"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "j = np.arange(5)\n",
    "2**(j + 1) - j"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "这些操作当然也比你用纯Python实现好快得多："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "100000 loops, best of 3: 11 µs per loop\n"
     ]
    }
   ],
   "source": [
    "a = np.arange(10000)\n",
    "%timeit a + 1 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1000 loops, best of 3: 560 µs per loop\n"
     ]
    }
   ],
   "source": [
    "l = range(10000)\n",
    "%timeit [i+1 for i in l] "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**注意：数组相乘不是矩阵相乘：**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 1.,  1.,  1.],\n",
       "       [ 1.,  1.,  1.],\n",
       "       [ 1.,  1.,  1.]])"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "c = np.ones((3, 3))\n",
    "c * c                   # 不是矩阵相乘！"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**注：矩阵相乘：**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 3.,  3.,  3.],\n",
       "       [ 3.,  3.,  3.],\n",
       "       [ 3.,  3.,  3.]])"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "c.dot(c)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**练习：元素级别的操作**\n",
    "\n",
    "* 试一下元素级别的简单算术操作\n",
    "* 用 `%timeit` 比一下他们与纯Python对等物的时间\n",
    "* 生成：\n",
    "    * `[2**0, 2**1, 2**2, 2**3, 2**4]`\n",
    "    * `a_j = 2^(3*j) - j`\n",
    "\n",
    "#### 1.3.2.1.2其他操作\n",
    "\n",
    "**对比：**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([False,  True, False,  True], dtype=bool)"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a = np.array([1, 2, 3, 4])\n",
    "b = np.array([4, 2, 2, 4])\n",
    "a == b"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([False, False,  True, False], dtype=bool)"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a > b"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "数组级别的对比："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a = np.array([1, 2, 3, 4])\n",
    "b = np.array([4, 2, 2, 4])\n",
    "c = np.array([1, 2, 3, 4])\n",
    "np.array_equal(a, b)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.array_equal(a, c)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**逻辑操作**："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ True,  True,  True, False], dtype=bool)"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a = np.array([1, 1, 0, 0], dtype=bool)\n",
    "b = np.array([1, 0, 1, 0], dtype=bool)\n",
    "np.logical_or(a, b)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ True, False, False, False], dtype=bool)"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.logical_and(a, b)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**[超越函数](http://baike.baidu.com/link?url=3aAGiGcMZFhxRP7D1CWzajcHf-OVCM6L6J1Eaxv1rPxFyEYKRoHXHdcYqKfUIc0q-hcxB_UoE73B5O0GyH1mf_)**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0.        ,  0.84147098,  0.90929743,  0.14112001, -0.7568025 ])"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a = np.arange(5)\n",
    "np.sin(a)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "-c:1: RuntimeWarning: divide by zero encountered in log\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([       -inf,  0.        ,  0.69314718,  1.09861229,  1.38629436])"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.log(a)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([  1.        ,   2.71828183,   7.3890561 ,  20.08553692,  54.59815003])"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.exp(a)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**形状不匹配**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "operands could not be broadcast together with shapes (4,) (2,) ",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-74-82c1c1d5b8c1>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0ma\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m4\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0ma\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m2\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m: operands could not be broadcast together with shapes (4,) (2,) "
     ]
    }
   ],
   "source": [
    "a = np.arange(4)\n",
    "a + np.array([1, 2])  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*广播*？我们将在[稍后](#Broadcasting)讨论。\n",
    "\n",
    "**变换**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.,  1.,  1.],\n",
       "       [ 0.,  0.,  1.],\n",
       "       [ 0.,  0.,  0.]])"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a = np.triu(np.ones((3, 3)), 1)   # 看一下 help(np.triu)\n",
    "a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.,  0.,  0.],\n",
       "       [ 1.,  0.,  0.],\n",
       "       [ 1.,  1.,  0.]])"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a.T"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**警告：变换是视图**\n",
    "\n",
    "因此，下列的代码是错误的，将导致矩阵不对称："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {
    "collapsed": true,
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [],
   "source": [
    "a += a.T"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**注：线性代数**\n",
    "\n",
    "子模块 `numpy.linalg` 实现了基础的线性代数，比如解开线性系统，奇异值分解等。但是，并不能保证以高效的方式编译，因此，建议使用 `scipy.linalg`, 详细的内容见[线性代数操作](1.5. Scipy：高级科学计算.ipynb)：`scipy.linalg`。\n",
    "\n",
    "**练习：其他操作**\n",
    "* 看一下 `np.allclose` 的帮助，什么时候这很有用？\n",
    "* 看一下 `np.triu`和 `np.tril`的帮助。\n",
    "\n",
    "### 1.3.2.2 基础简化\n",
    "#### 1.3.2.2.1 计算求和"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "10"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x = np.array([1, 2, 3, 4])\n",
    "np.sum(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "10"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x.sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "行求和和列求和：\n",
    "\n",
    "![reductions](http://scipy-lectures.github.io/_images/reductions.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1, 1],\n",
       "       [2, 2]])"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x = np.array([[1, 1], [2, 2]])\n",
    "x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([3, 3])"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x.sum(axis=0)   # 列 (第一纬度)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(3, 3)"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x[:, 0].sum(), x[:, 1].sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([2, 4])"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x.sum(axis=1)   # 行 (第二纬度)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2, 4)"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x[0, :].sum(), x[1, :].sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "高维的处理，思路相同："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.2671177193964822"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x = np.random.rand(2, 2, 2)\n",
    "x.sum(axis=2)[0, 1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.2671177193964822"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x[0, 1, :].sum()     "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 1.3.2.2.2 其他简化"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- 以相同方式运作（也可以使用 `axis=` ）"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**极值**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x = np.array([1, 3, 2])\n",
    "x.min()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x.max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x.argmin()  # 最小值的索引"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x.argmax()  # 最大值的索引"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**逻辑运算**："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.all([True, True, False])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.any([True, True, False])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**注**：可以被应用数组对比："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a = np.zeros((100, 100))\n",
    "np.any(a != 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.all(a == a)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a = np.array([1, 2, 3, 2])\n",
    "b = np.array([2, 2, 3, 2])\n",
    "c = np.array([6, 4, 4, 5])\n",
    "((a <= b) & (b <= c)).all()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**统计:**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.75"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x = np.array([1, 2, 3, 1])\n",
    "y = np.array([[1, 2, 3], [5, 6, 1]])\n",
    "x.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.5"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.median(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 2.,  5.])"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.median(y, axis=-1) # 最后的坐标轴"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.82915619758884995"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x.std()          # 全体总体的标准差。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "... 以及其他更多（随着你成长最好学习一下）。\n",
    "\n",
    "**练习：简化**\n",
    "\n",
    "* 假定有 `sum` ，你会期望看到哪些其他的函数？\n",
    "* `sum` 和 `cumsum` 有什么区别？"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**实例: 数据统计**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[populations.txt](http://scipy-lectures.github.io/_downloads/populations.txt)中的数据描述了过去20年加拿大北部野兔和猞猁的数量（以及胡萝卜）。\n",
    "\n",
    "你可以在编辑器或在IPython看一下数据（shell或者notebook都可以）："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "# year\thare\tlynx\tcarrot\r\n",
      "1900\t30e3\t4e3\t48300\r\n",
      "1901\t47.2e3\t6.1e3\t48200\r\n",
      "1902\t70.2e3\t9.8e3\t41500\r\n",
      "1903\t77.4e3\t35.2e3\t38200\r\n",
      "1904\t36.3e3\t59.4e3\t40600\r\n",
      "1905\t20.6e3\t41.7e3\t39800\r\n",
      "1906\t18.1e3\t19e3\t38600\r\n",
      "1907\t21.4e3\t13e3\t42300\r\n",
      "1908\t22e3\t8.3e3\t44500\r\n",
      "1909\t25.4e3\t9.1e3\t42100\r\n",
      "1910\t27.1e3\t7.4e3\t46000\r\n",
      "1911\t40.3e3\t8e3\t46800\r\n",
      "1912\t57e3\t12.3e3\t43800\r\n",
      "1913\t76.6e3\t19.5e3\t40900\r\n",
      "1914\t52.3e3\t45.7e3\t39400\r\n",
      "1915\t19.5e3\t51.1e3\t39000\r\n",
      "1916\t11.2e3\t29.7e3\t36700\r\n",
      "1917\t7.6e3\t15.8e3\t41800\r\n",
      "1918\t14.6e3\t9.7e3\t43300\r\n",
      "1919\t16.2e3\t10.1e3\t41300\r\n",
      "1920\t24.7e3\t8.6e3\t47300\r\n"
     ]
    }
   ],
   "source": [
    "cat data/populations.txt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "首先，将数据加载到Numpy数组："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {
    "collapsed": true,
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [],
   "source": [
    "data = np.loadtxt('data/populations.txt')\n",
    "year, hares, lynxes, carrots = data.T  # 技巧: 将列分配给变量"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "接下来作图："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x1070407d0>"
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x103768910>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from matplotlib import pyplot as plt\n",
    "plt.axes([0.2, 0.1, 0.5, 0.8]) \n",
    "plt.plot(year, hares, year, lynxes, year, carrots) \n",
    "plt.legend(('Hare', 'Lynx', 'Carrot'), loc=(1.05, 0.5)) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "随时间变化的数量的平均数："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 34080.95238095,  20166.66666667,  42400.        ])"
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "populations = data[:, 1:]\n",
    "populations.mean(axis=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "样本的标准差："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 20897.90645809,  16254.59153691,   3322.50622558])"
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "populations.std(axis=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "每一年哪个物种有最高的数量？："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([2, 2, 0, 0, 1, 1, 2, 2, 2, 2, 2, 2, 0, 0, 0, 1, 2, 2, 2, 2, 2])"
      ]
     },
     "execution_count": 111,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.argmax(populations, axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**实例：随机游走算法扩散**\n",
    "\n",
    "[random_walk](http://scipy-lectures.github.io/_images/random_walk.png)\n",
    "\n",
    "让我们考虑一下简单的1维随机游走过程：在每个时间点，行走者以相等的可能性跳到左边或右边。我们感兴趣的是找到随机游走者在 `t` 次左跳或右跳后距离原点的典型距离？我们将模拟许多”行走者“来找到这个规律，并且我们将采用数组计算技巧来计算：我们将创建一个2D数组记录事实，一个方向是经历（每个行走者有一个经历），一个纬度是时间：\n",
    "\n",
    "![random_walk_schema](http://scipy-lectures.github.io/_images/random_walk_schema.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {
    "collapsed": true,
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [],
   "source": [
    "n_stories = 1000 # 行走者的数\n",
    "t_max = 200      # 我们跟踪行走者的时间"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "我们随机选择步长1或-1去行走："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-1,  1])"
      ]
     },
     "execution_count": 115,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "t = np.arange(t_max)\n",
    "steps = 2 * np.random.random_integers(0, 1, (n_stories, t_max)) - 1\n",
    "np.unique(steps) # 验证: 所有步长是1或-1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "我们通过汇总随着时间的步骤来构建游走"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {
    "collapsed": true,
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [],
   "source": [
    "positions = np.cumsum(steps, axis=1) # axis = 1: 纬度是时间\n",
    "sq_distance = positions**2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "获得经历轴的平均数："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {
    "collapsed": true,
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [],
   "source": [
    "mean_sq_distance = np.mean(sq_distance, axis=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "画出结果："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x10b529450>"
      ]
     },
     "execution_count": 126,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1095366d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(4, 3)) \n",
    "plt.plot(t, np.sqrt(mean_sq_distance), 'g.', t, np.sqrt(t), 'y-')\n",
    "plt.xlabel(r\"$t$\") \n",
    "plt.ylabel(r\"$\\sqrt{\\langle (\\delta x)^2 \\rangle}$\") "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "我们找到了物理学上一个著名的结果：均方差记录是时间的平方根！"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='Broadcasting'></a>\n",
    "### 1.3.2.3 广播"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* numpy数组的基本操作（相加等）是元素级别的\n",
    "* 在相同大小的数组上仍然适用。\n",
    "    **尽管如此**, 也可能在不同大小的数组上进行这个操作，假如Numpy可以将这些数组转化为相同的大小：这种转化称为广播。\n",
    "    \n",
    "下图给出了一个广播的例子：\n",
    "\n",
    "![numpy_broadcasting](http://scipy-lectures.github.io/_images/numpy_broadcasting.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "让我们验证一下："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0,  0,  0],\n",
       "       [10, 10, 10],\n",
       "       [20, 20, 20],\n",
       "       [30, 30, 30]])"
      ]
     },
     "execution_count": 127,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a = np.tile(np.arange(0, 40, 10), (3, 1)).T\n",
    "a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0,  1,  2],\n",
       "       [10, 11, 12],\n",
       "       [20, 21, 22],\n",
       "       [30, 31, 32]])"
      ]
     },
     "execution_count": 128,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "b = np.array([0, 1, 2])\n",
    "a + b"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "在不知道广播的时候已经使用过它！："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 2.,  2.,  2.,  2.,  2.],\n",
       "       [ 1.,  1.,  1.,  1.,  1.],\n",
       "       [ 1.,  1.,  1.,  1.,  1.],\n",
       "       [ 1.,  1.,  1.,  1.,  1.]])"
      ]
     },
     "execution_count": 129,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a = np.ones((4, 5))\n",
    "a[0] = 2  # 我们将一个数组的纬度0分配给另一个数组的纬度1\n",
    "a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 1.  1.  1.  1.  1.]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([[ 2.,  2.,  2.,  2.,  2.],\n",
       "       [ 1.,  1.,  1.,  1.,  1.],\n",
       "       [ 1.,  1.,  1.,  1.,  1.],\n",
       "       [ 1.,  1.,  1.,  1.,  1.]])"
      ]
     },
     "execution_count": 130,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a = np.ones((4, 5))\n",
    "print a[0]\n",
    "a[0] = 2  # 我们将一个数组的纬度0分配给另一个数组的纬度\n",
    "a"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "一个有用的技巧："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(4,)"
      ]
     },
     "execution_count": 133,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a = np.arange(0, 40, 10)\n",
    "a.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(4, 1)"
      ]
     },
     "execution_count": 134,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a = a[:, np.newaxis]  # 添加一个新的轴 -> 2D 数组\n",
    "a.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0],\n",
       "       [10],\n",
       "       [20],\n",
       "       [30]])"
      ]
     },
     "execution_count": 135,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0,  1,  2],\n",
       "       [10, 11, 12],\n",
       "       [20, 21, 22],\n",
       "       [30, 31, 32]])"
      ]
     },
     "execution_count": 136,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a + b"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "广播看起来很神奇，但是，当我们要解决的问题是输出数据比输入数据有更多纬度的数组时，使用它是非常自然的。\n",
    "\n",
    "**实例：广播**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "让我们创建一个66号公路上城市之间距离（用公里计算）的数组：芝加哥、斯普林菲尔德、圣路易斯、塔尔萨、俄克拉何马市、阿马里洛、圣塔菲、阿尔布开克、Flagstaff、洛杉矶。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[   0,  198,  303,  736,  871, 1175, 1475, 1544, 1913, 2448],\n",
       "       [ 198,    0,  105,  538,  673,  977, 1277, 1346, 1715, 2250],\n",
       "       [ 303,  105,    0,  433,  568,  872, 1172, 1241, 1610, 2145],\n",
       "       [ 736,  538,  433,    0,  135,  439,  739,  808, 1177, 1712],\n",
       "       [ 871,  673,  568,  135,    0,  304,  604,  673, 1042, 1577],\n",
       "       [1175,  977,  872,  439,  304,    0,  300,  369,  738, 1273],\n",
       "       [1475, 1277, 1172,  739,  604,  300,    0,   69,  438,  973],\n",
       "       [1544, 1346, 1241,  808,  673,  369,   69,    0,  369,  904],\n",
       "       [1913, 1715, 1610, 1177, 1042,  738,  438,  369,    0,  535],\n",
       "       [2448, 2250, 2145, 1712, 1577, 1273,  973,  904,  535,    0]])"
      ]
     },
     "execution_count": 138,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mileposts = np.array([0, 198, 303, 736, 871, 1175, 1475, 1544, 1913, 2448])\n",
    "distance_array = np.abs(mileposts - mileposts[:, np.newaxis])\n",
    "distance_array"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![route66](http://scipy-lectures.github.io/_images/route66.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "许多基于网格或者基于网络的问题都需要使用广播。例如，如果要计算10X10网格中每个点到原点的数据，可以这样："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.        ,  1.        ,  2.        ,  3.        ,  4.        ],\n",
       "       [ 1.        ,  1.41421356,  2.23606798,  3.16227766,  4.12310563],\n",
       "       [ 2.        ,  2.23606798,  2.82842712,  3.60555128,  4.47213595],\n",
       "       [ 3.        ,  3.16227766,  3.60555128,  4.24264069,  5.        ],\n",
       "       [ 4.        ,  4.12310563,  4.47213595,  5.        ,  5.65685425]])"
      ]
     },
     "execution_count": 139,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x, y = np.arange(5), np.arange(5)[:, np.newaxis]\n",
    "distance = np.sqrt(x ** 2 + y ** 2)\n",
    "distance"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "或者用颜色："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.colorbar.Colorbar instance at 0x10d8d7170>"
      ]
     },
     "execution_count": 141,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10375a350>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.pcolor(distance)    \n",
    "plt.colorbar()   "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**评论** : `numpy.ogrid` 函数允许直接创建上一个例子中两个**重要纬度**向量X和Y："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "metadata": {
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([[0],\n",
       "        [1],\n",
       "        [2],\n",
       "        [3],\n",
       "        [4]]), array([[0, 1, 2, 3, 4]]))"
      ]
     },
     "execution_count": 142,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x, y = np.ogrid[0:5, 0:5]\n",
    "x, y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "metadata": {
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((5, 1), (1, 5))"
      ]
     },
     "execution_count": 143,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x.shape, y.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "metadata": {
    "collapsed": true,
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [],
   "source": [
    "distance = np.sqrt(x ** 2 + y ** 2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "因此， `np.ogrid` 就非常有用，只要我们是要处理网格计算。另一方面， 在一些无法（或者不想）从广播中收益的情况下，`np.mgrid` 直接提供了由索引构成的矩阵："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0, 0, 0, 0],\n",
       "       [1, 1, 1, 1],\n",
       "       [2, 2, 2, 2],\n",
       "       [3, 3, 3, 3]])"
      ]
     },
     "execution_count": 145,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x, y = np.mgrid[0:4, 0:4]\n",
    "x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "metadata": {
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0, 1, 2, 3],\n",
       "       [0, 1, 2, 3],\n",
       "       [0, 1, 2, 3],\n",
       "       [0, 1, 2, 3]])"
      ]
     },
     "execution_count": 146,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1.3.2.4数组形状操作\n",
    "\n",
    "#### 1.3.2.4.1 扁平"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "metadata": {
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 2, 3, 4, 5, 6])"
      ]
     },
     "execution_count": 147,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a = np.array([[1, 2, 3], [4, 5, 6]])\n",
    "a.ravel()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "metadata": {
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1, 4],\n",
       "       [2, 5],\n",
       "       [3, 6]])"
      ]
     },
     "execution_count": 148,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a.T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "metadata": {
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 4, 2, 5, 3, 6])"
      ]
     },
     "execution_count": 149,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a.T.ravel()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "高维：后进先出。\n",
    "\n",
    "#### 1.3.2.4.2 重排\n",
    "\n",
    "扁平的相反操作："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "metadata": {
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2, 3)"
      ]
     },
     "execution_count": 150,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "metadata": {
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1, 2, 3],\n",
       "       [4, 5, 6]])"
      ]
     },
     "execution_count": 152,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "b = a.ravel()\n",
    "b = b.reshape((2, 3))\n",
    "b"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "或者："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "metadata": {
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1, 2, 3],\n",
       "       [4, 5, 6]])"
      ]
     },
     "execution_count": 153,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a.reshape((2, -1))    # 不确定的值（-1）将被推导"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**警告**： `ndarray.reshape` 可以返回一个视图（参见 `help(np.reshape)`）, 也可以可以返回副本"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "metadata": {
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[99,  2,  3],\n",
       "       [ 4,  5,  6]])"
      ]
     },
     "execution_count": 155,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "b[0, 0] = 99\n",
    "a"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "当心：重排也可以返回一个副本！："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "metadata": {
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.,  0.],\n",
       "       [ 0.,  0.],\n",
       "       [ 0.,  0.]])"
      ]
     },
     "execution_count": 156,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a = np.zeros((3, 2))\n",
    "b = a.T.reshape(3*2)\n",
    "b[0] = 9\n",
    "a"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "要理解这个现象，你需要了解更多关于numpy数组内存设计的知识。\n",
    "\n",
    "#### 1.3.2.4.3 添加纬度\n",
    "\n",
    "用 `np.newaxis`对象进行索引可以为一个数组添加轴（在上面关于广播的部分你已经看到过了）："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "metadata": {
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 2, 3])"
      ]
     },
     "execution_count": 157,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "z = np.array([1, 2, 3])\n",
    "z"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "metadata": {
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1],\n",
       "       [2],\n",
       "       [3]])"
      ]
     },
     "execution_count": 158,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "z[:, np.newaxis]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "metadata": {
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1, 2, 3]])"
      ]
     },
     "execution_count": 159,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "z[np.newaxis, :]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 1.3.2.4.4 纬度重组"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "metadata": {
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(4, 3, 2)"
      ]
     },
     "execution_count": 160,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a = np.arange(4*3*2).reshape(4, 3, 2)\n",
    "a.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "metadata": {
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5"
      ]
     },
     "execution_count": 161,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a[0, 2, 1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "metadata": {
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(3, 2, 4)"
      ]
     },
     "execution_count": 163,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "b = a.transpose(1, 2, 0)\n",
    "b.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "metadata": {
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5"
      ]
     },
     "execution_count": 164,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "b[2, 1, 0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "也是创建了一个视图："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "metadata": {
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-1"
      ]
     },
     "execution_count": 165,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "b[2, 1, 0] = -1\n",
    "a[0, 2, 1]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 1.3.2.4.5 改变大小\n",
    "\n",
    "可以用 `ndarray.resize` 改变数组的大小："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "metadata": {
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 1, 2, 3, 0, 0, 0, 0])"
      ]
     },
     "execution_count": 167,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a = np.arange(4)\n",
    "a.resize((8,))\n",
    "a"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "但是，它不能在其他地方引用："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 168,
   "metadata": {
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "cannot resize an array that references or is referenced\nby another array in this way.  Use the resize function",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-168-59edd3107605>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0mb\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0ma\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0ma\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mresize\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m4\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m: cannot resize an array that references or is referenced\nby another array in this way.  Use the resize function"
     ]
    }
   ],
   "source": [
    "b = a\n",
    "a.resize((4,))  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**练习：形状操作**\n",
    "* 看一下 `reshape` 的文档字符串，特别要注意其中关于副本和视图的内容。\n",
    "* 用 `flatten` 来替换 `ravel`。有什么区别？ (提示: 试一下哪个返回视图哪个返回副本)\n",
    "* 试一下用 `transpose` 来进行纬度变换。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1.3.2.5 数据排序\n",
    "\n",
    "按一个轴排序："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 169,
   "metadata": {
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[3, 4, 5],\n",
       "       [1, 1, 2]])"
      ]
     },
     "execution_count": 169,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a = np.array([[4, 3, 5], [1, 2, 1]])\n",
    "b = np.sort(a, axis=1)\n",
    "b"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**注**：每行分别排序！\n",
    "\n",
    "原地排序："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 170,
   "metadata": {
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[3, 4, 5],\n",
       "       [1, 1, 2]])"
      ]
     },
     "execution_count": 170,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a.sort(axis=1)\n",
    "a"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "象征索引排序："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 171,
   "metadata": {
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([2, 3, 1, 0])"
      ]
     },
     "execution_count": 171,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a = np.array([4, 3, 1, 2])\n",
    "j = np.argsort(a)\n",
    "j"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 172,
   "metadata": {
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 2, 3, 4])"
      ]
     },
     "execution_count": 172,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a[j]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "找到最大值和最小值："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 173,
   "metadata": {
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0, 2)"
      ]
     },
     "execution_count": 173,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a = np.array([4, 3, 1, 2])\n",
    "j_max = np.argmax(a)\n",
    "j_min = np.argmin(a)\n",
    "j_max, j_min"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**练习：排序**\n",
    "* 试一下原地和非原地排序\n",
    "* 试一下用不同的数据类型创建数组并且排序。\n",
    "* 用 `all` 或者  `array_equal` 来检查一下结果。\n",
    "* 看一下 `np.random.shuffle`，一种更快创建可排序输入的方式。\n",
    "* 合并 `ravel` 、`sort` 和 `reshape`。\n",
    "* 看一下 `sort` 的 `axis` 关键字，重写一下这个练习。\n",
    "\n",
    "### 1.3.2.6 总结\n",
    "\n",
    "**入门你需要了解什么？**\n",
    "* 了解如何创建数组：`array`、`arange`、`ones`、`zeros`。\n",
    "* 了解用 `array.shape`数组的形状，然后使用切片来获得数组的不同视图：`array[::2]`等。用 `reshape`改变数组形状或者用 `ravel`扁平化。\n",
    "* 获得数组元素的一个子集，和/或用面具修改他们的值ay and/or modify their values with masks"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 174,
   "metadata": {
    "collapsed": true,
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [],
   "source": [
    "a[a < 0] = 0"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* 了解数组上各式各样的操作，比如找到平均数或最大值 (`array.max()`、`array.mean()`)。不需要记住所有东西，但是应该有条件反射去搜索文档 (线上文档, `help()`, `lookfor()`)!!\n",
    "* 更高级的用法：掌握用整型数组索引，以及广播。了解更多的Numpy函数以便处理多种数组操作。\n",
    "\n",
    "**快读阅读**\n",
    "\n",
    "如果你想要快速通过科学讲座笔记来学习生态系统，你可以直接跳到下一章：Matplotlib: 作图(暂缺)。\n",
    "\n",
    "本章剩下的内容对于学习介绍部分不是必须的。但是，记得回来完成本章并且完成更多的练习。\n",
    "\n",
    "## 1.3.3 数据的更多内容\n",
    "\n",
    "### 1.3.3.1 更多的数据类型\n",
    "\n",
    "#### 1.3.3.1.1 投射\n",
    "\n",
    "“更大”的类型在混合类型操作中胜出："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 175,
   "metadata": {
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 2.5,  3.5,  4.5])"
      ]
     },
     "execution_count": 175,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.array([1, 2, 3]) + 1.5"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "赋值不会改变类型！"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 176,
   "metadata": {
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dtype('int64')"
      ]
     },
     "execution_count": 176,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a = np.array([1, 2, 3])\n",
    "a.dtype"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 178,
   "metadata": {
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 2, 3])"
      ]
     },
     "execution_count": 178,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a[0] = 1.9     # <-- 浮点被截取为整数\n",
    "a"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "强制投射："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 179,
   "metadata": {
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 1, 1])"
      ]
     },
     "execution_count": 179,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a = np.array([1.7, 1.2, 1.6])\n",
    "b = a.astype(int)  # <-- 截取整数\n",
    "b"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "四舍五入："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 180,
   "metadata": {
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 1.,  2.,  2.,  2.,  4.,  4.])"
      ]
     },
     "execution_count": 180,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a = np.array([1.2, 1.5, 1.6, 2.5, 3.5, 4.5])\n",
    "b = np.around(a)\n",
    "b                    # 仍然是浮点"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 181,
   "metadata": {
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 2, 2, 2, 4, 4])"
      ]
     },
     "execution_count": 181,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "c = np.around(a).astype(int)\n",
    "c"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 1.3.3.1.2 不同数据类型的大小"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "整数 (带有符号):\n",
    "\n",
    "|类型\t|字节数|\n",
    "|-------|------|\n",
    "|int8\t|8 bits|\n",
    "|int16\t|16 bits|\n",
    "|int32\t|32 bits (与32位平台的int相同)|\n",
    "|int64\t|64 bits (与64位平台的int相同)|"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 182,
   "metadata": {
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dtype('int64')"
      ]
     },
     "execution_count": 182,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.array([1], dtype=int).dtype"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 183,
   "metadata": {
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2147483647, 2147483647)"
      ]
     },
     "execution_count": 183,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.iinfo(np.int32).max, 2**31 - 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 184,
   "metadata": {
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(9223372036854775807, 9223372036854775807L)"
      ]
     },
     "execution_count": 184,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.iinfo(np.int64).max, 2**63 - 1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "无符号整数:\n",
    "\n",
    "类型|字节数\n",
    "-|-\n",
    "uint8|8 bits\n",
    "uint16|16 bits\n",
    "uint32|32 bits\n",
    "uint64|64 bits"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 185,
   "metadata": {
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(4294967295, 4294967295)"
      ]
     },
     "execution_count": 185,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.iinfo(np.uint32).max, 2**32 - 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 186,
   "metadata": {
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(18446744073709551615L, 18446744073709551615L)"
      ]
     },
     "execution_count": 186,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.iinfo(np.uint64).max, 2**64 - 1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "浮点数据：\n",
    "\n",
    "类型|字节数\n",
    "-|-\n",
    "float16|16 bits\n",
    "float32|32 bits\n",
    "float64|64 bits (与浮点相同)\n",
    "float96|96 bits, 平台依赖 (与 `np.longdouble` 相同)\n",
    "float128|128 bits, 平台依赖 (与 `np.longdouble`相同)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 187,
   "metadata": {
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.1920929e-07"
      ]
     },
     "execution_count": 187,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.finfo(np.float32).eps"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 188,
   "metadata": {
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2.2204460492503131e-16"
      ]
     },
     "execution_count": 188,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.finfo(np.float64).eps"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 189,
   "metadata": {
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 189,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.float32(1e-8) + np.float32(1) == 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 190,
   "metadata": {
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 190,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.float64(1e-8) + np.float64(1) == 1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "浮点复数：\n",
    "\n",
    "类型|字节数\n",
    "-|-\n",
    "complex64|两个 32-bit 浮点\n",
    "complex128|两个 64-bit 浮点\n",
    "complex192|两个 96-bit 浮点, 平台依赖\n",
    "complex256|两个 128-bit 浮点, 平台依赖"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**更小的数据类型**\n",
    "\n",
    "如果你不知道需要特殊数据类型，那你可能就不需要。\n",
    "\n",
    "比较使用 `float32`代替 `float64`:\n",
    "* 一半的内存和硬盘大小\n",
    "* 需要一半的宽带（可能在一些操作中更快）"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 191,
   "metadata": {
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1000 loops, best of 3: 1.41 ms per loop\n"
     ]
    }
   ],
   "source": [
    "a = np.zeros((1e6,), dtype=np.float64)\n",
    "b = np.zeros((1e6,), dtype=np.float32)\n",
    "%timeit a*a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 192,
   "metadata": {
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1000 loops, best of 3: 739 µs per loop\n"
     ]
    }
   ],
   "source": [
    "%timeit b*b"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* 但是更大的四舍五入误差 - 有时在一些令人惊喜的地方（即，不要使用它们除非你真的需要）"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1.3.3.2 结构化的数据类型"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "名称|类型\n",
    "-|-\n",
    "sensor_code|(4个字母的字符)\n",
    "position|(浮点)\n",
    "value|(浮点)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 194,
   "metadata": {
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 194,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "samples = np.zeros((6,), dtype=[('sensor_code', 'S4'),('position', float), ('value', float)])\n",
    "samples.ndim"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 195,
   "metadata": {
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(6,)"
      ]
     },
     "execution_count": 195,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "samples.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 196,
   "metadata": {
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "('sensor_code', 'position', 'value')"
      ]
     },
     "execution_count": 196,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "samples.dtype.names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 198,
   "metadata": {
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([('ALFA', 1.0, 0.37), ('BETA', 1.0, 0.11), ('TAU', 1.0, 0.13),\n",
       "       ('ALFA', 1.5, 0.37), ('ALFA', 3.0, 0.11), ('TAU', 1.2, 0.13)], \n",
       "      dtype=[('sensor_code', 'S4'), ('position', '<f8'), ('value', '<f8')])"
      ]
     },
     "execution_count": 198,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "samples[:] = [('ALFA',   1, 0.37), ('BETA', 1, 0.11), ('TAU', 1,   0.13),('ALFA', 1.5, 0.37), ('ALFA', 3, 0.11),\n",
    "              ('TAU', 1.2, 0.13)]\n",
    "samples"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "用字段名称索引也可以访问字段："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 199,
   "metadata": {
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['ALFA', 'BETA', 'TAU', 'ALFA', 'ALFA', 'TAU'], \n",
       "      dtype='|S4')"
      ]
     },
     "execution_count": 199,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "samples['sensor_code']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 200,
   "metadata": {
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0.37,  0.11,  0.13,  0.37,  0.11,  0.13])"
      ]
     },
     "execution_count": 200,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "samples['value']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 201,
   "metadata": {
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "('ALFA', 1.0, 0.37)"
      ]
     },
     "execution_count": 201,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "samples[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 202,
   "metadata": {
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "('TAU', 1.0, 0.37)"
      ]
     },
     "execution_count": 202,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "samples[0]['sensor_code'] = 'TAU'\n",
    "samples[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "一次多个字段："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 203,
   "metadata": {
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([(1.0, 0.37), (1.0, 0.11), (1.0, 0.13), (1.5, 0.37), (3.0, 0.11),\n",
       "       (1.2, 0.13)], \n",
       "      dtype=[('position', '<f8'), ('value', '<f8')])"
      ]
     },
     "execution_count": 203,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "samples[['position', 'value']]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "和普通情况一样，象征索引也有效："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 204,
   "metadata": {
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([('ALFA', 1.5, 0.37), ('ALFA', 3.0, 0.11)], \n",
       "      dtype=[('sensor_code', 'S4'), ('position', '<f8'), ('value', '<f8')])"
      ]
     },
     "execution_count": 204,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "samples[samples['sensor_code'] == 'ALFA']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**注**：构建结构化数组有需要其他的语言，见[这里](http://docs.scipy.org/doc/numpy/user/basics.rec.html)和[这里](http://docs.scipy.org/doc/numpy/reference/arrays.dtypes.html#specifying-and-constructing-data-types)。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1.3.3.3 面具数组（maskedarray）: 处理缺失值（的传播）"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* 对于浮点不能用NaN，但是面具对所有类型都适用："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 207,
   "metadata": {
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "masked_array(data = [1 -- 3 --],\n",
       "             mask = [False  True False  True],\n",
       "       fill_value = 999999)"
      ]
     },
     "execution_count": 207,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x = np.ma.array([1, 2, 3, 4], mask=[0, 1, 0, 1])\n",
    "x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 208,
   "metadata": {
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "masked_array(data = [2 -- -- --],\n",
       "             mask = [False  True  True  True],\n",
       "       fill_value = 999999)"
      ]
     },
     "execution_count": 208,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y = np.ma.array([1, 2, 3, 4], mask=[0, 1, 1, 1])\n",
    "x + y"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* 通用函数的面具版本："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 209,
   "metadata": {
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "masked_array(data = [1.0 -- 1.4142135623730951 --],\n",
       "             mask = [False  True False  True],\n",
       "       fill_value = 1e+20)"
      ]
     },
     "execution_count": 209,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.ma.sqrt([1, -1, 2, -2])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**注**：有许多其他数组的[兄弟姐妹](2.2. 高级Numpy.ipynb)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "-----\n",
    "尽管这脱离了Numpy这章的主题，让我们花点时间回忆一下编写代码的最佳实践，从长远角度这绝对是值得的：\n",
    "\n",
    "**最佳实践**\n",
    "* 明确的变量名（不需要备注去解释变量里是什么）\n",
    "* 风格：逗号后及＝周围有空格等。\n",
    "\n",
    "    在[Python代码风格指南](http://www.python.org/dev/peps/pep-0008)及[文档字符串惯例](http://www.python.org/dev/peps/pep-0257)页面中给出了相当数据量如何书写“漂亮代码”的规则（并且，最重要的是，与其他人使用相同的惯例!）。\n",
    "* 除非在一些极特殊的情况下，变量名及备注用英文。\n",
    "\n",
    "## 1.3.4 高级操作\n",
    "\n",
    "1.3.4.1. 多项式\n",
    "\n",
    "Numpy也包含不同基的多项式：\n",
    "\n",
    "例如，$3x^2 + 2x - 1$:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 211,
   "metadata": {
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-1"
      ]
     },
     "execution_count": 211,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "p = np.poly1d([3, 2, -1])\n",
    "p(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 212,
   "metadata": {
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-1.        ,  0.33333333])"
      ]
     },
     "execution_count": 212,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "p.roots"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 213,
   "metadata": {
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2"
      ]
     },
     "execution_count": 213,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "p.order"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 215,
   "metadata": {
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x10f40c2d0>,\n",
       " <matplotlib.lines.Line2D at 0x10f40c510>]"
      ]
     },
     "execution_count": 215,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10d9d88d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = np.linspace(0, 1, 20)\n",
    "y = np.cos(x) + 0.3*np.random.rand(20)\n",
    "p = np.poly1d(np.polyfit(x, y, 3))\n",
    "t = np.linspace(0, 1, 200)\n",
    "plt.plot(x, y, 'o', t, p(t), '-') "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "更多内容见http://docs.scipy.org/doc/numpy/reference/routines.polynomials.poly1d.html。\n",
    "\n",
    "#### 1.3.4.1.1 更多多项式（有更多的基）\n",
    "\n",
    "Numpy也有更复杂的多项式接口，支持比如切比雪夫基。\n",
    "\n",
    "\\\\(3x^2 + 2x - 1\\\\):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 216,
   "metadata": {
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-1.0"
      ]
     },
     "execution_count": 216,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "p = np.polynomial.Polynomial([-1, 2, 3]) # 系数的顺序不同！\n",
    "p(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 217,
   "metadata": {
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-1.        ,  0.33333333])"
      ]
     },
     "execution_count": 217,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "p.roots()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 218,
   "metadata": {
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2"
      ]
     },
     "execution_count": 218,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "p.degree()  # 在普通的多项式中通常不暴露'order'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "在切尔雪夫基中使用多项式的例子，多项式的范围在[-1,1]："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 221,
   "metadata": {
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x10f442d10>]"
      ]
     },
     "execution_count": 221,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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MGuVXuYWpWITKSvZ84ufv18/7PJSV+SV6VZI1+d6HGeJVE4pR1aQNQ+59Ear86tyzpbHRrz8P85qIcq6oL2qU36iIXCLrN998k+bOnk0xyyIA9PWvfz3wtPfdd59D5uPz82nn669TdvK3AOiduXPVfZS3kDiBZcuW+VYGP7v1Vm9nRP1WQbiy7l0VZMXVTNnZ/vgC+TelpfYkz3PRq4KQuHTPVVxi4+6NYW6XUVYZqnYNUoYh976IMI+Vpib7peI1QrkPs7yUDyL7dNQvUX4TIvkfOuMMGtqvn49E15x7rtqHvKGBGufMcY77z+S1zWHS+y/++7/9fQT0aijF6mXVY4+RBb/aZ2huLn3yySdu+2HukKogK0Gm3FDMiVY2qkYpVyh7/PAiH3Pm+A3v3JArVjScpOVJS76nTU3hOfcNIsGQ+/GKIMLlxCheRuGxwr1QOInzCEaeXKulJThIRybhKFK/jrijrhgqKuhHCgIFQKMyMugjBWnuBSiTSenbJkwgammhHw0b5ny3YMECbx+HDrWl3/p6W5rWRaImz/ExQDX5+U57pQD1Z3371cSJ/lQEOndIQYScBEUWSFlC59J2RoZLzvy+qcoVyu6PTU3BGS35vePHCT09Gwvls2LQqTDkfrwi7CUSxqviYvvlC8pDznXH2dn6qkLCRU4XYdnRMPQo11RXR4nMTIoz0rx2yBAqYp9vUZDm/TU1zv5p/fs7aoyN7HclJSXU1tam74+uf8lz/Dcj9v4A7czMpBtY+2MBOspdNbk/ukp3rpvkWlpc6T0Wc8e8tta+d5x05Qldd6/EvQ1Lpyw/O1wgUNkNjI97l8GQ+/GKMNWGSmdLpK5iJHTH8biXHLh6gNcGlRNoyd4jQuqLmm9Gvibdb5PnWcoIc0RNDR0++2y6nX03pqDANWAmSfOSBQuc/TfccINzvnaABmdlOfv+8Ic/6MdYN+aJBL0/fz6Vs3Zuzcwkam2lRFMTFTIj7tqpU9X3RXfvwvYLiVteqelSK/PfihUajz8Qkwy3J6hIOpFQ2234JGWk+C6FIffjFVy3qXr5VDpbovAMf1xyq6zUS/GAvSrYvt0vxQlDn+7FXrSI2mbOpC0zZ9L6556jl19+mdatW0etzz9PRxcsCHQJPARQOSPy+++/nyiRoA/OOYcKmNpl3e9/7zlXnBHv+gsu8ARzXXnppc6+pUuXuueT1UeJBLVdeCG99+abdOTIEc8l/du//ZvTxlCADrG+L7niCmffl4cOje5GGovZEnpmpq0eEgQs8u8ARBMn0j82baI77riDFi9eTEsnTqSPgqpAhZG/6tqD0iOkU0nLoFNgyP14RZjXgkpnSxT+wsnubCopPj/fL63JLneVld4JgIho0SJaX1pK9QBlM4LmW1lWFv17VRXt10jIdxcVOccOGTzYo0b514oKZ99nRM3WeJyeY+0Pzsmh9tNO8/T/wQcfdPZPnz7dOx5spbPlL3+hE044gQBQLBajU8rL6d4xY+i7o0Z5ruFeqe+///3vnX01ALWff354nhzVPcjJ8Xm3/GnmTCosLPScfxJAW3XScpgLqwpBz0yQ4Tton4x0Moz2cRhyP14RlPMjCFFeOC4Zcslf+H8LL4n8fHflMGOG1+Wuf3/fhHPDsGGUoSF1eRuRn09/WbvW06329naaNGGCc8wPfvADz/4/TJ/u7MvPyKADyfNfw9pdArheQsmJZ+/evZSRkUEAKCMjg/Zxn/TkJLoJoMG5uaH9vqSmhtqFETY5xocPH6aiWMw5ZsNzzwXfI+7BEjDJbpswgQZqgrIGZWXRe2++qW8/yEguBzClWzkrqoFc7o/YdLn7DYjIkPvxC07S6UhIQS+c7D7J0wuo/L9lKVMx4XDpWGyVAJ0E0NS8PJqelUUDpf2FhYX08ssvO6deuXKlsy83I4P2vvWWp9vtn/40ncj02zcBdGjiRBrEXCZ/M3Kk365ARNOmTXOOWbFihdtoQwN9AtAJOTnOfoupf/h20vjx9OHppytJ8+ILL3SOW7ZsWfA94mM8YIDr0ihsEfX11Hb++TTlpJOcNuPxOH3lK1+hrOQkBYAuHDDAtj0ERdjyPDK6lASpxECkY1zn/ZHr7RpdvRaG3HsrgiIB020nSH+qSiEL+ANNxIpBJNviaWZ5tCWbcLZu3UpFTJ0yIyuLXpePnzGDjgK0HKBCJuXG43H64x//SPv27aMKpnb5D9WLX1dHdzGyHZCTQ7d+//vuZNKvH328a5fSo+M7I0c6x33mM59x20wk6KdTpjj78vLy6Nlnn6Xdu3fT9d/4Bk2PxWgebCPv/vnztd4iD86c6bQxcdy4wFv26umn088A2j5hgncsGVneNmmS0152RgatPekkooYGemLsWM+Ec98pp+h96OVEbqqJW77/qRh7g/TxHLw/ra36qFoDDwy591bodOoq32MivUQlv2xRqi81NekLIwui2b7d26+AHO1zWPDQ8OHDab9qmc9IceO6dVTKgotisZij6wZAgwDar8oa2dBAhwEawqRXvt0qokT5SidJJOvYcYMHD6b29nYiIvrwww9p0KBB7mrgppt85/SMp8YQ+X7//tSPneNvf/ub7769/eqrnrHKzc2lG2+80bUrJM/194kTPXr2/6yp8dzjRXyCKyujA7pUvfL4CXWcZdkTdmOjV7DgmT5V0aVy4XV+n4Mk/pYW+1ni+Wii1BPo4zDk3luh0qmrfI+D6nsS+fXn3G1Od75UXRcDfJqfffZZh2gyAPrTs8/62xGh97m5Tk6cP69ZQyUlJUqSfujUUwMNeLczn3axlVoWfTBnjv93yXFrA6iYqVtan3+eiIj+8z//05X88/JstYsufW/Q+NTX04WsP9ddd519THICOATQBKZS4ttFF11ke+ckEnR0wQJqqK939o0ZM4YOn322+3zU1dH7JSVUxX5/zeDBwSmFBWR1XHOz190xqFQfuxbl/iCJ32STTAuG3HsruDQj+6NzMpX1o/LLoApU4QjK8a5BIpGgK664guaceSYtGT7cNnwqXtD29naazoycn9GtPDQJpbZu3er5fZZl0XeuucaRqnX4aPBgOkEiyB/oiEWMaWkpNTNy/3RVFe3cuZMKCgqc7342alRq0qRkF3no1FOdtkZmZlJ7SYmj0voyc9XMAKi6utrT/8tHjaKPTj+dbho92vP9s88+65d0KyrofnZMNkBbovRZ5T6rMuzGYh4PKMf4qqoCJbcdtI/HN6RrxO1DMOTeWxHmjy4Toqq+J5Fav9rBiNKFCxd6CCYzM5PWCiMl05M+Pneuh2Dezstzz8Vzlwu3SRFGzwigra2Nfj12LK0EbPfIKH2cMYM+BOgJ2EbVu/v1o6M6YmH67BekCWES02uPKyigttmz1QQV0Svko48+8qhT/pQci6fLyjznvePmm6mtrY3+/d//3fN9sdS/r33ta/77nJTe25uaaPrJJzvHzi8pCVaPiNXTgAG2OkbsE5OfSF8hPyvyc6hTz0VxmeTPYkWFiXANQZeRO4BfAvgngI2a/WMAvAjgYwBfDWinywehVyJsWapKGauC6qVKx+iVxG9/+1sPwTh68IoK2sXcH49mZtJ4tv+LooydkAr5i8xD8lX50VXqn7w82/UyFrM/q44XYxNFCkz+pkFxbQDoKUCv0gpSOUhk2tLS4rR5DkB78vKoknniNM6ZY69MFi2iozNn0pWDByv7M23aNDsRGV958XFsbqaXXnrJ85tnnnlG39egQCVhY1E9K1zaF66x6RKxHAyn0rubsn0OupLczwAwOYDcywHUArjBkHsaCHNv1O2P8vBHjVKUcOjQIRo6dKhDFvF4nMqY1Dk7FqMjAFFGBt3HSCU/I4P+efLJ7osqF8QQ0qFKpxuWCpdPUlwPrkvyFTTeAwbQnxVEej4ntKCSd6oJUiLNF154QUnWAKi8vJx27drl+d1RgH46ZQr1T6ptsiyLLrvoItq9e7ffP1xBvp/+9Ked9idMmECfCP283NeGBvoAoF8OG0aP/O//0nvvvee9Dl05wETCvlc8A+X8+eHjrbsH/BpUxn+T0sBBl6plAAzTkTs7Zqkh9xB0pjQS5eHnE0MK5+Z+5qU5ObT7tNPo/2prPX7f1wG0MxajakZa3776ar1XiQiAEknOZIKQc9Prgntk6S4oSEeHZJ+WXXstFRcX00knnURXX3UV7RUpklX9qauz1UpS4JIDYdBmxUS+9KUvKcn9N8uXa0v47du2jR6fMYN2vv662oguEr4xozQlErRjxw7Ky8tzzrHs2muVk/mLTz1FI1jis4yMDPre977nHhC2OuETc05OahWcFPfAeUbkSZqrn/q4m6Qh996AzpBGUqnrmc65Fy2iC5iU/p3qaud33znxRA9J9S8u9kj3iSD3OzlgB3CNebJrnezNEYupjXi6IJ1UxlGlk5bHV5UrXzYY82OSk8GhuXNpgjRmy8T4877n5flXH6pozuxsv06c3c+bb77ZOU9mZqYnMIyI6IEHHqAYiy3wTDhTpnhJNWx1Im8y0YeNL/9+9GhvOmPx/Bg3SSLqJeS+dOlSZ1u9enWXDkiPhO7FSbciEk8lG/XcIVkcD8yYQTnspf/bGWc4EtSRs86i+tNP9xFDZmYmPfLII9GvXfaL5iRWXe1mnCwocH2wVaoCXZBOEMJcSvn4CqOhHNAlcqnz33JJk5Xr+8d559FXa2roBwBtA2xyFvnaRZuKSFpPP4qL7RWOKi8/e5aOHDlCp7P7M3DgQNqwYQMdPXqUfvrTnzqpFwBQUVYWjWWS/gCA3jv//Ggun/LGcxCpJi8+EaqEDH7/s7Lcc4sxKi72Fpk5zqX41atXe7iyV5B7n4Gu6HAUqTYVNzaVBKmDWPoKgtKc69543HnhTxo71idB7e7fn84pL3eOycrKCif2qNcej4f7YHO/6jB7BZH/XsjtK3TSzvfCqCvcR+WALr5akCVNQfTCxVUygvo8RuS+qCYuOcJTce1bt271uHX2y8ig4ZJf/Yknnkjbpk2jPbADxcT3S664wh0zFZG2tLh5hSZMcCst8Ujn0lL72sRY8JgNrm6bPNkdX9FmMn2yA7mgSB+V4nsCuS8z5J6ELse6DqkEcnBjFH9xopB8BDVGIyP3GydM8PaPne+dc86hX/3qV7Rx40b1uaKmVZBJLMwHm1cDCoNKtSHGLj/fbiuo2LZK7y5IRrVa4N473PDIDcuC8LkRURXhqYpHiGhAfv7556mYqcz4dlIsZicaS/bnCebBk5OZSbtOPTXaqmbAAO/kJ4ieE3JVlTcwjxcgb2ryttevn388+epGHNsHg526jNwBPABgJ4BPALwL4DMAlgBYktxfkfz+AIAEgHcAFCja6aahOEaImrNFBRHIVFkZzfNDvOQ8ayN/GXWSV4gaI5FIUBYzmr61YYPXe0J+wYLUSWGpivm1SLnUfSkR+HhyolXpc4OkdBEqH1UK5JOuTFqq6+cBaZzIhBGWS+9NTWqi1qkzgtRJipXi+vXraSwznOYA9A2APhATXHIc2gGqZeT/LT5eulVN0H1VGdRVCefC9PtE3jHXuaf2AXSp5N4Z23FP7vyl7N/fHyQioMpnLQyMqUj7KgOleEnCfJk1kvR9Z57pvORThdQuh5rzFyxInZRuqmIVZNc5XUCWvGISvyku9t6LiPYHD1lHcdfjn4WqgQd0yeqXoHB87pXCYwdUxKtZKbbv20fvDBlCa8aOpX/yNM3imOS5Vgwf7tz3OEAfTJjgTq5yRa8wF0aVqoxL4Pw+hKnVTGoCIjLkfuyhUF2EZtOTiUB+cYMk4yAf9rCXQkPKFzIvme+PHx/eVtA+2dWto1IXn5h0Sa2kykVa337RlqpINJ/4wiRHcb7MTFc9IT8DfOKNcp9E32QyF5sqmI1L1JZFNHWq2oApNuGumTzXkT17aDgj+P/m+fPlZyXKfZWfWy68qHLVhN3zdJ0PjhMYcj/WEA9imJQqe17U1rov0PjxtreIkCaDUgYE6V/TkIg+vPJKyoO7PH/9pZfC2+oM0k4VQUmrZDIZgXxyAAAgAElEQVQOg2pCFgWpOTHrfK1V+fDlZyBqiTsZsiqKS9OqdrgroUI6V+5juOOOO5x7X11d7ctQmZL0LE8IXJU0f37HCJob19MNouplMOTeUxBGeGL/woX+hGGypBWWMkCnn2Voa2ujRx99lK6//nr65je/afs+K/r46Lhxzss9pqDAf65U/JbTRZRz6CZP7isvPDai2C44GctbY6NePz96tFd1wn32VW6bqULYHoShMmxsRT+Fmyb31BHPERcopPY+HDWKypi9Zfny5W4/okYBi/skjMlihSDGV4yRauWo8zCTwSeKVFYBvRiG3HsbdPrqVFIGhKiCdu/eTWcyPTpgZyJcNnw4Hdmzx9NUI3Nv/MaXvxy9v50ZJh7lHDrDGpfodN4eOnCdvggSEkSkk1y5F06/fmr9N0+VMHq0/ZucHE9kaYchCFVE0La2+ickMYkFPU/FxbSMPSdjx46lgwcPEi1aRB8XFdGTAH0XoG+NHeumTpChUgGJSS5IHaXyatLdNzFRqPL8H6cw5N4TIEueUXXmqahUOITBT3hhMAPh9tZWqpEKKvPt8upqJ6Xuxo0bne8ty6I33njDfy5df1NZtodJ+R05h+wuqZPuhXQoPJOEYVukFqir80rKuvshCDQvTx1YJZOUqnZokGdTVMgToryCGTjQe72685SV0W6Actkzct5559Hdo0dTjfTsDBs2LPgZ4bEUKqKWx1SeFIISk6WykjhOYMj9WCCstF2QVNsZ+mo5mjKpA/474MkfAoAWAzRTekmXLl1KRESXX365890FF1ygPpeuv6lcR5gaibcle2mEnUNIdPn5NoGrcsAEhc9HkRo5RJSpyk9epU6T1SYqj5l0CkXzKE454Rp3vQybVJLXc+eNN2oFAr6Vl5fT22+/7b/2oMySOtWLmBSysvxpCFToY0nFDLkfC8gPmZw8qqtdueT2GxpoC0CjWGBKNkCPJvt4ZOBAupJlewRAs0tLKZNFML744ot2213hlRDmUdSR/POJhN+fWp5EdMZFvongrlQKREeZ8MRkwCNLuZSdLlkNHOj+VpeFk08qQXrvJL7yla/4yLw/QP8Wi1EuS2Fw+eWX68dCNQa64D6V91J2tl4672MukobcjwXkh0wmJPkB72zClNpfNWcOlTOjWAyg34j+JPv4ySef0FxWXINvM2fOdNvuCq8E0V9RtINlUCSi1IzJKshqAXkSEUbK+fO9qgNhAOTBXWHSYWdIjzLZpUNWsieKGGORj2fAALURMoAgjxw5QjfU1lIjQPMA+iZAe5O/f4o9LxkZGbSpuTlaZk55IlMZvXmQXXm5t8/83Vm40L5nJSV9ImOkIfeuhI6UZfJO07+8ozhw4AAtXrzYQ9Q5AD0CKP2iP/jgA7qsstJz/Cnl5fROebm7ZJZDxaOOSZQJTCeVp2JMVkE2HAa5pbKyezRtmj4pmS7moCPSo5x5MqwQSxC4OooTHZ+cVTaIMHUaf1aFH3+yjbmzZzvPzcXchz0oMydvr7LS6/suhAfeJ3nS0kU8i8/Hsf7dkHtXIgopR3GBCyMM0U6U0PrkvmeeeYaqqqo8RF0ai9G6hx8Odcl8fMAA+lRZGf183Dg6ctpp3hdGvHyyV4Lw+uBugDwdgJwQKxXDaUfsEFFVA/K5VBON6l4GBfOk2kdOXLpydVEhq6PENfBzNDam3leuB29t9Vwvr/xkAfSmvOrh16qaDFtawotwy+6TOnWTnHpDd/5eDEPuXQU517guCVYEn3PPklmXJySKO2By37rzz6ccpoYBQP8yeDD9Qxhzw7x2VGoQcZ0LF9pLY3nZK3t9yOkARLpefg3y+YOIMdWXMiyFb1i7qokmKGFYKpK6fE4+xulK/SqorkEmx6BxUHl5hQgq8+bNc565L4wc6b+OoMmQ7ysuVvePJ07j/Vm40FsDVrc6O46Mrobcuwqyj7XqxefBNapc30Ft8kmjosKVaOSXkhsDJ0+mtzZsoPJkSTYAVJadTQ8++KDt3hjVa0dWg/CEXfKyXvSFZzssLfVncgTcY4SUFqaTDTKkhpF9VL21avINyxNfVubVzesShumuR55w+OfKSn9b6RaKloWGIA8j1bMgjw3vp0ZQeeqpp5xnLz8Wo4ROf666H8LxIBbzpviVi3aIlR/vH5f4g1ZQx5HR1ZB7Z0F+2eQ8JvyhkcPdVbm+g2px8t/KUp0gVC7JJKMV2/ft8wQnlcVitHX9evcaFF40ygc9SIJWSeBE3sIS/AXjBlBdxK3OAyLIkBpm2BXXFqa3Vnnq6OqBqlQ2mZnuKiZIKlQF5GRkuAZEeeLW6ZLTUTFEkVZVz4L4jhOnPFZSe+3t7TSORTX/UD6OJ12TVwi6yV7OsST2q+5dmFdTS4t65dkL1TWG3DsLupetutq/XNSRqM7rQkgiMhnKBiSdFJX8/dNPP+28VJmWRS88+WSwX3iYflj1wHODakmJ+yLx/nCS4udQpQuIx9UVh4iCDalh4ebiJZaDknTGb76M1xmN5TB6+Z5wFY0sbfP7LZNVdrY+6VdQ9syoKoYo0qrqWRDf8fGorAzNk/SLM85wnsP+AO3JzLSvuaTE25ZqhaBqV55c5Dq8/FkK82rqjojqboIh986C7mVTGd5kQgta4otNqHLkYgziXOIB51IK29pLS+kUVohh8ZAhamlSlVo4ir1A9E8EwAhPCfkcQq2ggqzKEmOkIp8w/W5YuHlQYJLq5eX3TNe2rk2uMhAqGnnc+QQvr3LkPsnX3lEVQ0cM0kT+8Qhp79AZZ9AwuPaez6nGjDsPiIlalwRNjOW4cer9qv6ERTXLSd+4yq2XRLkacu8s8AeI/5+um6MspevcAMW5ZDc+vhUU0G/hdXd8F7Db5+lmVYUqxAOtOreunidXzcgl0oJeiFTUQGGSVBhhyZOnyOooIjaDILctPIGExM317bGYO6YqlYYsZfJnRvRJHg9d+cBUx0BGKl5YuvNEOb6hgR5lz2MGQL8EqD1J4Eebmqh93z77WHnC74zrDPpNIqF+3lUqtx4uwRty72pEJRlZKg1T5QSdT7z8kybZaqGsLGpkL9MXOanJOn/xW0FUxcUeX2XPufmDLo6fPFm/cuFBJSo1iGqsUvFWSQV8nOJxO5d5ui8t1wVblt8oqkpalkiEp17WGTd520LtFyZJRsmeqFIFpqqOULUhI5Gg9gUL6OyzznKeSQBUYllUXFRElmVRYVYWzS4poR/k5dFWgNqnTqUje/bQjh076MMPPwzvR0cQ9Gz1IoOrIffORqrh56qXXPVCpSKd8GPr6mg7bL9i8RJtld3zVLprLonrMiqqDFZNTcErF5XqIog0dK6i4hxCZVRV5Sa7qq+P5kGSygpLQEjpZWWuhC/Gk+d0F6uXoPbS1eNyd8WoXkLyuKukYHk1w9VFquuIYvQP6NM777xDJ8RiHoLXbf369aOcZHoMy7LoxBNPpCeeeCL6mKWCoHetoyqsboQh93QR5SVSPdhhLmW6FyrdyM6GBvo2e0nmzp7tn1C2b3c/C4laEFaQjlE2WIVVVkr2x6O6CCNTcbzsKqpyG+RbUL3TVAOXOLiUXlVlfyfyv8gEGmRf4NeWamZMLtHLbei8hGTSVXkQyarAMP25TgiJUhYwiYPTp9PnYOcyikLyfIvFYvT8eef5XUJ1xnHdeEZVJ/UydBm5A/glgH8C2BhwzO0A3gTQCmCy5pguH4S0oCNxLhWPGuXPVhfkUhb0QqleENl9TiH9te3eTYNYQrCHdAmgVBI1yxgZKF2ms6rgE4rqhRJqBDmqVbiyyf3leu7CQr0qSTdmUV9uXcpecW3c00WXW0eldgs6Tvbjz852zy3Gc9Qovysll84TCTd6WGf7GD3aHsfsbK8fuQ5RbSQR1ByHpkyhvW+9RXtPO43aAHoboHuys6khHqdClqCufyxGGYzgB1iWbT+SJ3Sx6VRD/PnJyfG/p1x9mOqKsIegK8n9DACTdeQOoBHA75L/TwPwR81xXT4IKUOOPpXJRnhE8JctK8sb/LJwod4FMWi5q4uI1OTneHTOHOdFqADoE9Ukw/3yRdGJMF/3zpB0gtQSqslGuLKpPCi2b/emqtWpknRjFlVFokvZK6CL8Ay7btV4qvz4+TPGIRM79+SJOpnwNrKzw+9t0KQe5GIbtE8TD3Jg/nx6//33iRoaaDtA5UydcwXgJgOTJ3rd/VSpFIOEB9UE0pcNqgCGBZD7zwB8in3eBGCg4riuHoPUIEt+QdnyuC526lR/BF8UYhNSmupFUvnHS5gXjzsvwTd5/yS9vHM+OZ+I7gXuSPZH2R9c5aHC1QiyC5zsQaFaNelISTdmqapIdMbJKISn6qeK8Lk3Umurq8cXRlt+DtFmZqa/X1EnLpXtIF0Cizpxy5K1zg4iJObkBPXUI484z3U/gPaI6xWrwrDsoLJKUUwQ4pkQ5+YSvews0Fcldwon9ycAnMY+rwIwVXFcV49BatBJfrKBr6HBfiGrqvx+y7W1bvpaQWyqAB6x5eT4HqQjR47QDd/6Fp0zaBA9cNdddPToUV9Xt23b5hhSLYC2zZmj1ctHemD5UpU/9GKCSye8XkcAiYQ3rYGqv8IewNvLyQl2ZdSRb5hqSaciSYUAVas7+Zr4PZBVYtu3e91M+Xm3b7dVRWLlxfdHvb+y7aAjBBZkN5LVJ7rVi04AaW6m9vZ2msoqhv3w+uu954+qKhTPGS8wL87N+ynsJ6r3vIeS/LEm9xns8yoAUxTH0dKlS51t9erVXTwkGsgPpk5aVkksqlB3+cWVpVE5jJ95ihw+fJiam5udBxsAnXLKKbTz0ks9D9w3v/lNZ/+8s87SX1vUF0FWDQjJJkp4vTw2QiJNh0REdCmPZoxKtkETUNA+nYokzHefI4hkg1ZmQfYZ3aSjEjyi9jPV46O2Ieu55evgK2I5N41iLH75k584z3dNTQ0dOXLE34+OpGDQBTTpjj/GWL16tYcrj7VaZiH73LPVMvKDGTU9L5H9VzYeysfJn7dvd18AKVfHlVde6SF2h+AzM+lQ8rid555LBQUFzr5HHnnE39dU3TZ1niniAQ+TEOVJLp3c6/K9EO3x3OOyVKUjQJlAgl5Y2V1Ut6oIQiqrA52UyMdMJkTutx8WiNUZSDV5mcrlVnUdfFWYm2vfs/p6n3T90UcfUf/+/Z1n/He/+53/nDpXWvkaVKoyWXrnz4RM/MKgzV1kjzGOJblzg+r0Hm9QVfnvqqB7gWXikI9T/U7WC9bW0lsbNpDF0vWeVVJCmYzgLwbokylTqGXhQue7sfn51HbZZcEGuzBJu7nZVSUB6uCmMPLqDIlw0SJ3VVNQYNsIQpbwSqlbldwqVcm6s6G7H1G+F4QeNQiss/srZ11UnT9KxsmsrJSMmV/96led5/yiiy7y91FlOOVqIO7dpHJdDfII4v3h1y8bu48RutJb5gEAOwF8AuBdAJ8BsATAEnbMTwBsge0K6VPJUE8i90TCa6RJVefWkcg2RixXX3218zDX19dT+7x5dLskwZ+Ql+f5/LTmxQhcdqr2y4FNqRJeujnXdROSblIKWhUFVVvqDgIPgu4Z0d0nbhzm3lddqT7gxmThnaTKuqg6f1gRFjFB8XdNkG9WlivdM1XYxo0bnec8Ozub9om0BaKvwjtNZUeQnyWeolog6Jngth9B7ioX2WOELpXcO2M7ZuQeFOgi5xqPUn0+6CGJqB75cO5cipeUOA/zE088QdTSQu3xOC3p189D6GK7QDzQMpmJB59LLjzPvMqolE59Uo6oLoBBx/MVlE7fncqqqCcZw3R90qkHglYrqXgQEaWnmxbSrni2srNtAYC3ITx+giaYIPWTKomaVBu1dvJk53m/44471H3t3z88OV+qE6BQt8p1dYOyjHYjDLnrkKoPtir1axSjXYRAJHG+uxhpDx8+3DYgJfe1A/T9WMyTZuDMM86gf553nq0PlINT5GvQJSfT6UlVCNPBhvnq80lSFUtAZP9NR9/d2xHVnqGKCs3ODi8IrXvedXUKxCbUYs3N6udHDr7KzbV16qJgS1gfVBHNknB1ByP3SQUFdHTePO+KTZdfXjxL/fq554gqdYdFSKc7YXQiDLnrECGqzrNxX2+dFCUb9GR9sK5QcPJ8M5mB9Ec/+pG/n9Om0TqAbgRow5w57u9VIfNCoiosdF9QnTGYG76CJi3Z2yFI0tSNZWOjfwyDMh/2FaRqzwgjNiK1+21QTnhVBCi/N0HePfG47eHEfyu7+MourlyC1xVyicdp71tvOXlnANCvxLUqbFbK8Yua6THIHVaoZcQkJIzCQSU2uxiG3HUIepnEckyQWV6eWheqkqJk1YYqqEbhNfHuOec4htSMjAzatWuXfSyvHKN7iFUh82F55lWIupoRQTRBLxQfS/6SVFaqx9AgNUQhNnkCVRljhWSan+/q2IUvvZzPPkz9FRbfoFoByKpCIuU7c+211zrkXpGVRbu3bAnuF4c8qehUKnIJSTEGwvOrpsZWI/EgsMbGY+Y2acg9XahUMzqSDHIB40UXVEu93FyigQPpltxc5+Gtr6+321VFy6oeYlXIfBB56vJ6BxX8VhnFonpuCNLIzfXnTDHE3jFEMQhGqTXAV35heYF0kIPzwgz5UYrdJPHBBx9Q5aBBzjvSr18/amhooK997Wu0adOmaGOkqmXAyZi/l3Ikt2685s8/ZoKKIfdUIRuqxDJM96AS6YmOEfPhCy+kIwMH+h+O5FYLV5d+1xln+CcBnUpHB5W6RUgscoFmIq/UIksjOTn2tQwYEKyz1BlUp02zsz5OndqjI/6OO4RNwHKulrB0CVHPt317cPoN3i8dMSpsVw+wSmN8q8jJoUQUXbpKv8/PKcZD1LWVn1Xxe7GyEbaiYySoGHJPFXIIufygRvGcYcTeCtDFWVkUi8VoZEYGreeEmXzQNrEHNSsri/addpqX/INc4VK9Jr7xh5sTPpdG5MRLQS96mEH1GCxdDcjr3ijXh5X13QJBkbJRnr+g9AQqD7UglRKzXT0A0GQFwf9Hv37hfeOTj85rSWc4jvL7boYh91QRZDSKSk7JB3E9QHnSQ1gA0JMjRnh0eJ9jKpmm0lIP8TvG0LBIPB3k4tWAPfHI3ihytkNZpysklih5XbhKJ0g6NOgeqCZYlaTMk6XJBMbbUOnJZahIO5XVgC6WIfmMbnr5ZbrxhBOc9yYjKUh1iuAQVc1yjHPEG3KXEZbEX2c0EmoLubhFQPreRk0VmtzcXHr11VeJ6upoD0C5bN8z8ksoVgoqSVp+yaIEBelC11XEnEjYn+Vc2LqxFFAZ8nqItNMnwYkxrAi1jnx1evIoK1lVG2G/kd/DRMLnItu+bx+dNWCA8+5cnJXVOQFGUdUsxzj/jCF3GWGh6zqSVC3ZKiq0BqGXWP1Iy7Lo3nvvpaFDhzrfjRw5khL19XQ9I/ZJSBYR5qHOol1Zki4o8C6x5WuR/Yh5UrN0xycoOpE/3MYTpmdBQYy+ZzwseEylJw8iNlVMhMg339ISXu81Iv70pz85708pQEdLS8Ml6Sj1ZqMgquqpi2DIXUbQci+IJOXfi40XjmY3s7Gx0ZUoLr6YaNEieq2sjPIZmVcOGuSpOvO/gkBbW/0ulbwgAy9awY/h+cFT8UoJ8odWuXLqxlLAeML0fKhyIUUNHuMr2SieOLJxNUqKiYhlJ48ePUoDsrOdd+gvUSTpKOePgo6qnjoIQ+4yVMs9WeUSFNLNj5cLRyexbds2V2oH6PUzznAk/AfgV9MAoDE5OXR46FB/cIcgdrmKDmCHXJeWutJH1GANGUH+0Dp1jWosDXoPwlZXYRJoIhHsLsnb1wkMulVCUFQ39+oqLSWqqqKLS0ud9+iHYuUbpJ6JkuJCNxZhAUvduGrtm+Se6tJIpXLhxRZ0enlNAMlNN93kPGzzRHvsoby/upriTB9/hmXR39kEQICbClUVLScInqtlamrU4fy6ceHFhkU7qZaOM+i9CJuYdfd79Gg39S3PCyNHanPhRKc/lwtoqM4dj3ufVW7/SW6/ZELS3CjPqLxKCSNv3SpEZVjuRoGnb5I7n92DQtv5g7p9u9doGVbGTA5QYpgyZYrzsN0jyF/KEb5r9mz6DkB35OTYdU+lCcCzie+FLl7WyctGLp3eMZVQc51O1ujR+wZ095tL1HJBjlQFAH58Vpb7zsmqQJW3D0sH8DYj91zYxbgjP6NR0mqoViEqmxdvsxv07n2T3GWfbXlmFsYULgXk5fmzKMqeKkFBHslzbJ4503nQsrOzab9Oh6mS/rdv956ff889TvhDJZJG8YeOP6i6xF46F0VVFC036Bpi7xvQ3W+e6qK1VR+pHaS+0JXj0z1rcvCRsEs1N9vxH2VlnjTYzzz+uPp8YQKPLq0G708i4e93WL6eLiL5vknuss+2HG0pP1AielIlNeuITZNf5ntMimgSSbJkBBU4EH3XuaxxSSMvz+2n0JWrSF4V1i3+D9LtG0ndQIYq1QWRfyUb5Bevex/DUjyr3GmTbX2OvXfLli3z/j4oI6YQYrKy7BxOUSKxwzzQdEndOlmi75vkrpv5a2u9Ui9PACSMlGEl3VTnSD4kRwEazqooLZ82Td2/oOVrmITMVTciC59KypALdAdBJa2rAp0M+i7CiEl+pjnByeoLvq+uzg7Uk5+1qESYbGt5TY3z3jm5maRjAiVs2QMtTGcf9I7qbHKdbLfqO+Qe9DDwmyEGvKTEr8sTRlR+fJQbkjzmKSY9lAD00ZlnpmdRDwq04i9KY6PeYyEVz5mogU4GfRdh74H8TKvUF8KWFZZ7Juh8GueGty+4wHn38vPzqa2tzf1NIkHtCxbQ4/ffT8uWLaP33nvP32ceiZ1KUfQgBAmZRnJPAVH1XLJqIsxfV6NHXPf739Njjz1GBw8edI65MB53HrD/CHpwU5HOm5q818ZTkQZNPjrjcEBEbeRAJ4O+hzB9uryS5Tr1SZO8kjGX3qMWJxHQved1dTSECVevvPKK85N9+/ZRc3Ozs2/u3Ln+PsseNJ2FKDVm00TfIXe53BfgLbDBETTgQekIkg/WY4BTEamwsJCuvuoq2jh3LsWYe+Nr4uGT80dHSeyvS+KlqpYUFEikko5Uk4ExlhqEIZXVrJx8T+WUEFW1wd/LigrXSyY/3zdZXMzI/bbbbnOamjt3rvO92F5++eXIgVIpQf5tF7oQdxm5A5gHYBOANwF8XbE/DuBR2MWxXwIwTtNO51ypyhdcqsXoEKsq5S0Rtbe3031jxtDDSKYBUJSFOwI74EgViOT4rZ96qt9vnksbuuWm8OLhBRRUSZw4gl4SlbQVteamgYEO6Ujdquc0FRKVVYdyZHgi4SnHt6Cigqilhf7C3JL5dmFZmZYHIrtSh/UzygqlA+gScgeQCWALgGEAsgBsADBWOuZmANcl/x8NYJWmrY5fJbd6Cwu1Sm2hcmFirpF3n3iic/O/r5F47wsgdcBO2btmzRr7N7ILlypaT0B+eIWBN5UZX/bb10lbPEDLwCBVqFQwQYE8URLMhT3jPKqUS+2MfFtbW533cCDsPDOL2Ls5fvx4538LoE2cB3i/5JUzR9iElOoKpQPoKnI/FcDv2edrAVwrHfP/AJzOPm8BUK5oq+NXyR8SuYKKGOyyMtcYyd0M2W/Ploj6hdGjPe0cBWgU23/dddfRz372M+rXrx8BoIqKClq3bp3bL1Eir67OPV8ioTaCyiHRukkgaCnJ/fZFLVUBE4Rk0BVIweHAd0yqWSKFTlzzbhw5coTiWVnO+3nfuHGelNsvTJpE55SXO5+/Ul6ujpIV7YuVcyoTUlQy7wS3yK4i9wUAfsE+Xwbgx9Ix3wdwa/L/UwC0AZisaCutCyMiv+FGRYTC95aHSquK/gJUIaXorR4yhA4fPmwfl0jQn1gOi+KiIkokz7Vt2za6d9Ys2n3aaXr1D1fx6PTe3KCje0h0Dxf/PjPTW9avC4w5BgZEpCfosOLcARHeoQgg0Kuvukq5op6QmUntAD3JvhuUm0tHxKpafhdV6lOuIZCrNcmr5jB0gi6+q8j9wgjkXgjglwDWA7gXwMsAJiraoqVLlzrb6tWro19dmKqB79d5xSRJdee8ecqHYu3atc6hP7z+euf7Sy+9VH8uVeQdv4l8NZFqnUr5ZZInuKwsO3pP7pPJB2PQFYgihKhq/3bRs/nuu+9SFpPexfaz5Lna8vM9WSSf5gJRZaX7PsoTkhzgJ3v/8MhyUQQ+CGmspFevXu3hyq4i9+mSWuYbUBhVpd9sA1Cg+D7ShaU1QEFeJhIef/xxJbnfeOONrLkG5/u77rpLfa5YzJ/7Re6jeCHSyeIov0z8JREpFEw+GINjDZ0Q0g3P5hVXXOF5hxsA+uSkkxxC/iLbdzlAmzMyaC5AcwDaqJuQwtIU8He+vNx/TXIdY5HbvgPX3lXkHgOwFbZBNRtqg2oxgOzk/4sA3KNpK+2Lo5YWW2KNWrg6QM/1bWZpL2bqmXOHDiWqq6O2s8+mgoIC5/stW7a4PxZLTDkvDBDsOy4/4Kku7eQ25MnCuDgaHCsECSFhz2ZUfbSm6Mbrr79OucnSlbNnzqSPLrjAM6H8iRXa7peRQRVMki8CaNUJJ/jPKyczC7o+lbCmSn7WwRVLl5C73S4aALwB21D6jeR3SwAsSf5/anL/JgAPASjWtJP+1XUkC510/NksAGkpm9n7x2LUDtAf2XdDhgyh9vZ2dbtiKyhw65/qIB4QoaPnBtHsbK9/vE6fyVckRlI36KlI5dmM+l7rCHXRItpYW0sPT55Mn4hoVKbjbz/lFBqvWKU7BF9YSLt37/aeK4pfflBApMpzroPvaJeRe2dtKZN7mKEmCJoHrL29nfozPd0bY8dSKfNlfx2gGwcPdj63tLSo283P9y7Pokw4qmRdPOeNqi1dlJ6R1A16KlJ5NqNOBLKHmW6VIL9jFRW0EaCR7C+QiaEAACAASURBVB3Pzciggawe64MPPug73ccff0y/+MUv6JZbbqEPP/zQ/jKq04K4/k50bOjd5B5W8FllqAlqp75e6fq0detW56aWZGVR+759dB7zjLkrHqf6WbOcz3fffbe3fdm6DhAVFqZuNS8qso0x4jvVLM8t9hkZqU0kBgY9AVGqPEV5r2UPMwFNxlZHcCoqIhowgD5asIBuyMmhfwHoDwB9e+xY5x3/whe+4Onnc7/9LY0aNcrZ79ji0jUM91RXyM7cAsldNXBRZvXRo239dyxmt8F10QoXp9/85jfOTTvzzDOJiOgHJ5zgfHf2WWdRRkaG8/mdd96xz6NaRaSqU5N1eUTqWV6VuVFscuFqA4OejK724pJjScS7KVdx4lwQj9OTK1c67/jkyZOdfn4EUKHkJn3eeefZ50pXFdpTXSE7cwsk96hhzDJkNQfXhSlUObfccotz0z772c8SEdHzv/udUh83e/Zs9zzyKoKHLUcpdZeKKkXWL/LCBSaDo0FvQkdtQ1GkXtUKnwtgGRmuoJR8h/bv309WMmV3RkYGvT9nDhFA68aM8fHAuHHj7POkqwrtBPtY7yb3dAeOz8gTJwbXcySiz372s85Nu+WWW4iIqK2tjUaOHOm7qffdd5/r1SK8Y3iocVhmuXRnbDlzowlKMuit6KhtKOgdkhOMcSErkbCLynMhSYqPOemkk5x3/alHHiFqbqbbWU1kR0efm+t1qghDmFCXhpomXXLPQE9ASQmwYoX9NxW88gpQWQk0NgJr1wLV1W47ijbffPNN5/9Ro0YBAGKxGG677TZf0xf87GfAli3AgQNAWxvQrx/w9NNu2489BpSXA/Pn2+ffv9/bQF6e/be2FsjNBWbNUh8nY/lyoKYGyM8HDh8GiovTGxsDg2MN3Xu9eLH/fVB9J96hggIgkfC+O5s3A7t2AUeO2J+HDnXPU1ICTJvmHjt5MrBxo6cfM2bMcP5f19oKrFiBP//tb75LOHToEHbt2hX9mjdvtrnoySeBKVNsfjh4UL1/8eLgMeko0pkR0tkQJLkHgQcFDBzo83VVHquZFYcMGeLMyJs2bfLsO/fcc519SwYN8s76eXlqlUiQZMFnbDmCVhXsEbVdA4PeDtXzrUvVwVfnvIwfV72onBpCVtfLly/3qWB5wjG+vfDCC9GvLSgeRd4fMZIXvUotoyK2KBnl+JZKVRci+uijj5yblZGRQYc/8xnP+f7+97/T7MpKmlVcTO+JJd348bZXi07XzQtl8JBmmbxFKTzetyACN37sBscruBfY5Mlu7IeosVpU5H3fdGTZ1ORPDRD1/HV19A7zisvNzaX9+/dTRlIPbwE0j5H7vffeG/36uFCnSsWti1gNeOd7F7lzYhOzsS7nshwUID8UqoyLigHauHGjc7OGDx8eLj2octjIkXLbt6sLccvkzR/C7Gz7IQ3Kt2782A2OV8jvhcorjL//QWTJJ4kwIVHhhTYiP9/hhB/+8IfO/2MA+nphofN56dKl/usI050HFRJXVZALeOd7F7nrZmMV2XF3QbHMamnxesqIh4GlKWjft89zAx6+917nZp1dVaV+MESVp+JiooUL/SHPqkg5+eEU/VelIC4p8WatrKqKVrHJwOB4AV/t8vemsFAv7PD4FUUKbyf766JF3tQgPEe7/O7W1tKiyy93OKGcpQi+tH9/+vl//Zfz+dMDB4bnqJeLBcn8pLruiKuN3kXufJYShFpUZIfxs6IaWt26nNxHCny6Prm0GpmbS18A6J8A/WDCBOdmfWHwYP+DQeSdaOSkYPwG8UlB6P7Gj/cut8Q1ykvJoMALo183ON4h3gs5XmTIEDsRl+qdD4uDEZwh+7eXlvoTmLEcUFzvzrdba2tp1cSJzucZ8rkXLXKL7Ah9vy5brOAn+bpTyBTbu8idQzY6yDOsXAKPp72Vfb8bGqgNoH5J3ZnYLorH6V8//Wnn8+1jx3oJlv1eKVVMnKh3gQxToYRVbDH6dYO+iETCG5cSlDlVlXlSp/IQGyf6pible/qPf/xDSe7PT5pEb7HPgwQnBK3geR8FgXN+klcfKWSK7b3krtOj8U2ll8/JUVrIN7HcMGLLycmhSZMmOZ+fVGVw5A+MfPNSqa8YVAJPBaNfN+irUOnTVRKtqHSmIteyMq+kLDQAQhUK+EvoMYwYMcLDFdP796ej8+ZRG0Ax9v2HYSt4fi2qd1q2M6rUwhIefvhh+vGPf9yLyZ2n8x0wwB0AsexR6eUDZryHWX1U3bZV9VtdFGqqEjXXtckl8AwMDNRQqTFVjg5lZa4TA0+8V1HhFdiE9BxAnkREV0kVnd7885+dVcUI9v1f+cRTX29PIKnkaQ/y+iFSGmjr6+ud81OPJ/cwn25uDOE1Urm+TEwAmnJf383LCyT2EwFqnzrV/1t5Jk03skxIEbJvfCckEDIwOO7BbXDi/dGpS/kmS+cRV8QbLriAsi2LMgH6VTJqXfx+TrJmMgD6zbRp6RXdUfVHpYpV2BVmzJjRi8hdzH4690VxgZMmeUmV69dUN42l+ryIEfnXhw/3kfvKk08OnmB0KhhVIJIK27fbD6isMjKGUwODcKgIVDZGCrdo4WETIp0Hoq6O3gVos+K9XDRokMMbP548We23HkVYi5Jnik9gSfXTlClTehG5q9yAwnRVPEmXinilHM7jWFbHF596igYnK7UAoPKyMjp69Ki/jShGTa5nkyepKEFZxnBqYBCOoLJ9Is+S/DdVd2JVlleu60+2dwPTAnz1qqu8/KSK1VGdf/Ror3FXJ9glEr5MtmNZamLq8eSeatENIq8RlS292tra6De/+Q29OnWqs/+TWMxTNPdAfT396qc/JQAUi8Xo6aef9rcftTo7t+7Lk5RqAkql3JiBgYEN+T2JsuINEwAFVOm0RSZJRbrw+9iK/8ILL/S2pdOhy6t6boPLygp+/6WJbdiwYb2I3MWNk2faIH20CN2X8kd897vfJQCUbVn0BkAUj9NrTz/tDMYQ9kC88MILtGHDBvWApqIukZeHYpJSTUBGUjcw6DiivEcaAdAH2QuOS+v8nU7+//zo0Q6f1NbWOs2899579PGuXX4duti4f72Qxi2LaOrU4NWFNLENHDiwF5G7apBratTRpgIKHdzRo0dpMHN5/N64cUSJBK1YscL5bp7qgVBNIlFJOKjMlmyZX7TINvxmZdm/MeRuYJAeuDedLnJVpPcI073LgUyy14oQPJMr+XdffNHhk7KyMiIiWrlyJWVmZlI8HqeVK1fa7SYSXmcQPnnU1dkCKo9M58FQAeqkoqKiriV3APNgF79+E8DXFfvLAPwewAYAfwVwhaYd/yDLS5p43C/VK8h33bp1zkUDoLq6OqJFi2hpdbXz3dUnnOBtSw4JFnoyXSIfGVEzQMrHRlkRGBgYqBH03oXlgpIRJYCQtXmktJRiLCDy4MGDNIslHANAN910k/07IeAJIy8vjVlRoVZJh2gNuIqZOpvcAWQC2AJgGICsJIGPlY5ZBuBGcol+L4CYoi31IMvl5yLoqr/85S97BjgrK4s+OP10ms++u/vuu4NDguUVQZirYipqFuHOBbjRrQYGBqkj6L1TeJhExqJFdgpxeXUtuV0OZ5zy6quvUj5LNgaAsrOz6aOPPnJ5avt2f51lwK7pLK9AAq7tyJEjnvNQF5D7qQB+zz5fC+Ba6ZglAO5I/j8cwGZNW+pBjjKbshvSPnMmDWH+p2L7XW0tVUk3QhkSnJ1tR7vJFVzCqr5EMboKqIIUDAwMUofOEUG8kzyQSejbUy3Px995ya52JssOyTNH8m39c8952xa8IzgmJ0dd5D7AyeLgwYNdTu4LAPyCfb4MwI+lYzIArAGwE8AHABo0bXl7L4fph93M5A15STG4AOjiCy90/s/NzaW2tja1m+XAgd6bKshXN6lEdWXiMMZUA4OuBfeQkd/lMEGN56fiAh5Hki+uvPRSh1dqa2uV3HP/tGnK3x7YuJH+Y+RIGmhZ9GVxLtljRjMR7d69u8vJ/cII5P5tALcl/x8B4C0AhYq2aGlpKS3NyaGlubm0OjfXHdycnGgk2NBAN7MLHiItkcQ2Y8YM/cBxy3pJifu9blKRi3BHWf4Zt0cDg66FnIOKBz4GCVec+CsrvfY2BV8sW7ZMyTEW+/9bX/2q77ebN2+mqqoqz292ZGQQtbbq+5OciFavXk1f+tKXupzcp0tqmW9AMqoC+B2AGezzMwBqFW35iVK1LNIMMhERJRK0kJXKu3n4cMpWDPwXx4/XFwAR6pnMzGieLCr9mTGQGhgcW4j3eMIEv0OELFyFBS2J4/hqIKniueeee5TkziX6Cy64wEfS8+bN8/1m7YMP+q9DMxG98cYbXU7uMQBbYRtUs6E2qN4KYGny/4EAdgDor2jLT5RCXyaH8wbkcBg1apRzwS+deio1Kwb+Pr4qkAdOEQkWCJFOQNw8o2oxMDj24AQuV0jTpCZxVDdy0FJWlv07nkkyqeJZfc45fqndsmj16tXO5zF5eZ7Kai8/84xyQvCV6+O2vIULPQLthgsu6FpyJ5uUGwC8Adtr5hvJ75YAWJL8vwzAEwBaAWwEcImmHZsouWGhuNhOECYbNDUZGffv3+9cbCwWo0OXXkpPFRf7BnETb1+17FKVuAszwhhVi4FBz0SQ67GusI8ceAS4SQmZimfbtGk+fjnxxBM9Bs9MgA4DThry8847T0nu3x01yssfOm++5mZ6cfLkrif3ztoA2ATKa6ECblpcvjyRg4SS4LPoSfn5RPE4HU0OLh+Io4BtqW5t9ZI2n61lv9hUIlV1xmADA4Puhyq/uoBwSxZ8IMDTiQBE+fk2B0gqnrazz6bBEr98Ydw4oro6GsbyVv012c5LLPAIAH2epSD/V5lbuFeN5L337Cmn9DJyl3XuPC1uBMn4RyzL45WsnS+zMN3a/HwvSfMbqEsXzAda5S0jE7nJ2W5g0HOQSPgrpAnwGhHV1f7sjE1N3iL3Awb49Pev1NfTkiuuoAULFtAXv/hF2pNss5GR74qkUFnLvltQU0P/jyUfOysW89sGZFV1UxPRokX0W5Y0rHeQu9gyM6P7jDNczFJw/kTMtgMG0N4NG2jMmDHUr18/+s2UKV6S5kZVniOeKDilgICKyHU52w0MDI49+HvN9egiTYFQgwiSlz1vwlbuyeOvZuS7DKBf5OQ4n3NycmjrKafQX9kxI4cN87elWnXU1dFD0mqBegW5x+M+40EU7Nq1i6qZp8yLnHTLyqh93jz68JJL/MFGIvEYT/4vIOu8qqr8FnQVketythsYGBx78Pea69GFp4ycGoAfX1oaXrg62c4vWXrxOTk5VMGCK6+bOJEoHqcPGEFnZ2f7U46rVh0NDZ6MlL2D3EVinqBEYRIOHz5MN9xwA+Uy/VZmZiZ9NHeu/XueflflBROl8K6qwovIPSMS/xgiNzDoHdDZ71RqEICof397EmhsDOYLqcj1Zha9yrdBAH3IVgml2dnOvr9Pnx5c94GIKJGgX0yd2svInUhvvVbgwyuvpJMVA/iZyy7zhQl7Ugxw10rZM0alihG/E5ndiovVWdwMDAx6PoLsd3JqAFk4VHnSCchOF/X1dJaC3JdVVrqcUlBAUxiH/UHmFI0jx+23394LyZ0nCgtRz9wqEftJAD0K0NEFC9Q3kv8vZ4wTZK8KbBK/44SeboFsAwODngue4IsLdpzk8/LUqhnZ6SKRoIdPPdXDUTHLop2vv+5ZJVzA9i8XwqPQBKhqxhLRTTfd1AvJnROwIiKMoyEWcy7uy7m51JYK2co3Qs5FEeYxozOwGhgYHD9QaQB0qpmWFjvpIAuUamtr89SWuOiii9zjk3zCvflujKg2ltMeUBqcm4HuRkkJsGKF/ffwYfd7ewJw8Mknn2Bte7vz+QsTJiA2fz7w9NP2bzkWLwZmzQIaG4H9++3vli8Hmpvd4/m5Kiu97Yjft7UB1dVATg7wuc8Bd97pP5eBgUHvgYobOAQfrVzp8kVRkb2vttbmAIG33wZ27wZWrbLbBRCLxXDdddcBAHIzM/H1LVvsc11+OfD++0BFBaqXLHGa2C63qznXoUOHOn7t6cwI6WwQkjuHmC150p8knnvuOWfWGi7PbLIRQtZbqYwUAecKihQzMDDoxYjCDUTR3KIDEpK9+OKLtOnkk5U88thppzlcdvbAgX5f95oaW4IX3npVVfQfzO0bvUYtw/DKOefQRTk59I1hw2jr+vWefd+ZMsW5sEWyQVSuqCT06Krc7Lm59oAFVVxS5X03unYDg94P/m4HeerJ6U90KUgEEYvqbrqqcYxHNjBBddSoUf525fQJSc7rdeT+/e9/n2bNmkXLpkyhAtZ5C6Dbb7/dud4ZLIz3wf793cGWvW24zkpEl6nyRoTdNDFTt7TYEWslJalXdzEwMOhZ0DlZyJ56MmfoVu1Bq3yhkxc1U5PxNp48NJZFn1x2mXpSEKlZiorost5G7q+99pqnw/JWlYwAPXDgAGWy2oW7t2zx3wRRlo+XtBODzA2oci6bMFWLqX9qYHB8QuYODs4ZQat2Lp2LAMmsLJvM5UhXpgqqYnz2huyxJ3vwbN9OC6Rc8NTTyf2u8eN9hF5mWZ5CsIlEgp544gnn86QJE/w3gevD5ALbQnUjIlVbW4NvmqyDC0pCZGBg0HsRlr9KlQdepBKurPSrdzn38E0IlIJv6upoNuO8J046ybtfYQc4R0o1TD2d3D8rEfu4zExaP20aTWCZ0/7whz94qpBcffXV6hshBkQk/eFZ3yIU2XagOlaXhMjAwKDvQKEL96zmZXXKpEk2b8gG2aoq+hzjvVsKCryFuRWBTLNnz+4wuXerK+QreXnO/08B+OvRo5j00ksY98EHzvevvfYaVq1a5Xyur69XN7Z5M7B2revieOQIcMMN9v/iPMK9iLtfyhDHFhQAiYT9/2OPAY8+atwgDQz6ClQuk4yvHGRnAzt32sf99Ke2++Srr9p/V68GysttN8iDB93fVFfjBNbErQcPYnxbG65auxYf/+u/+jno8stx6JVXOn5N6cwI6WwAPOqXvYWFztLke9/6lvP9xRdf7PyflZVFBw8eVM+q8qzJy2bxmTNKEQ7j/mhg0LehSgMgVvEi+Vg87k9LEuaWTUTU0EC/1dgZZ2Rl0e7CQnoiI4NmAPSdpBZiUm8zqIpteH6+h4Afrq9XXnjdoEF+Yhb51fv3txP9iHZ0UWV8sEUysLCwYgMDg76DRYv87tQcLS22AFhZ6aYQVrldNzeruSSRoDcbGpQcB4DKpM+bABrdW8n9IsklcdPJJysv+ns8ib4g5sxM9zteKENH0Px73QQQoVCIgYHBcQpO0Mnaqdr98nGKfDMqLmlra6MYS6cStP0WoKG9ldx/CHh8ztvOPpuyFRf5ohhI2Z8d8BfK0BE0/95I6AYGBjKCeIFL9UyVzKXyqILhiBEjPPz2dYAqmIuk2O4qKqLy7iB3APMAbALwJoCvK/ZfDWB9ctsI4AiAEsVxTkefVei2JkqFrk8uKqJ2gCg729ZzCb34+PHp51c3ErqBgYEMFS8IN0iuKcjMtPXvnHtSqKeck5np4bijTU2091Ofou9UV3u+v+Fb36ICScqnziZ3AJkAtgAYBiALwAYAYwOOPxfAKs0+p6PvKGbJ2ZWVnov5v4cf9uvS5aLWBgYGBl0BnRukbLvjQZJcTaxw5LiaBSY1V1Z6zvNfjPs+//nP+1Q4lAa5h7lCngJgCxFtJ6I2AL8G0BRw/CUAHghpEwVNTb7sjmdlZzv/V+flYc6//Ivtvsizpm3caP8mKNNbWBY4AwMDgzBwN8hYzP1/8mQ7q+zatcCTTwIff+wef/rpLvf87W/uMckMkp8fMQLjAUzNy8OPV6/2nGfQ8OHOKXb8+tc4cuQIACAjowPe6kHMD2ABgF+wz5cB+LHm2DwAe6FQyZAkuX/88ce+ifLvZ55JJwBUnZ1Nf/3DH9wdqiWTpnqJdl+YO6SBgYEBBw9m3L6daOhQu75qfb03uWBrq1tPWU4+ptPPt7S4ka91dUTz59OauXMdfpzIuDI/qcqhLlDLXJgCuX8KwOMBbREAsiyLvvOd79Dq1au9g9nSQu1i8HQELEg6qHSeKqtj0GRgYGBgEAbZoyYsJXBQsR9Z5VNRQZsmT3YIvR8j99wOqGXYekOJvwMYwj4PAbBDc+xCRFDJFBYW4vrrr/fvePttWHv3uonwV6zwHyOiUgGgqspV7TzxBLBrl/19Y6MdLcYLbcgRqwYGBgapgEeRfvghcM89Nr+MGWNzT1YW8Mwz9n4eFR/UlsCuXRjEChN9zHaVWRbetYXjlBFG7q8AGGVZ1jAAO2FL5xfLB1mWVQxgJmydeyDyVCG99g77b20tkJtr6662brUrIxUV2ZWV+DEnngjMn29/9zEbjuxs/6AuX25PGKaykoGBQTpYvhwYNQrYs8cVQEtKgC1bgKNH7WPOOQd4991obV1xBfDii8B77wG1tShcuRK5NTWQ6y/1GzYM2LYtrS4HkjsRHbEs6yoA/wfbc+Z/iOh1y7KWJPf/PHnofAD/R0ShtaHy8/PVO8rL7a2kxCb1devs73ckFwqLF3tJev58V4ofMMD+O3kycPfd/raDZlEDAwODMEyf7jpoTJzocpAgdssCBg+2NQfl5XZJvrw8m7NkgbKkxM5ftX+/w2dWSQkqMjKwjUnwAJBbWJh2l8MkdxDRkwCelL77ufT5VwB+FeWEWnLn9QmzsuzvCguBDz5wpXkhqQNeKf6hh4Cvfc1I5gYGBl2DXbvs5IQA8PrrwCWXuDxlWUB+PvDSS/bnsjJbwgfUKubFi20Vs0T+gwoLse3AAc+huW+/nXaXu71AdqhapqDALlQNAGec4Ratffttr2sRL4BdXW0P4DXXGDdIAwODzgcn8rY2m4fy820OOu00NwtkPA5MmmT/r7PxCdshc5MEgIqZM32H5kpknwq6ndy1krsg6+nT7c+1tcD997uk/eqr9veTJ+vT+PJBGzvWELyBgUHn4JVXbCcOQcC1tbZRlcfixOPA+vXAypWu4BmUZryszE0fvH8/KoYM8R2aK9pOA91P7hs2qCVrQdaVlfZFy6Qtcq0PHaoesMWL3QkAsJdRbFY0MDAwSBvV1bax9LHH/MQtBNO33rKPC6ofwY8fPdq2LSYl+EGDBvkOza2rS7vL3a+W2bvXtxwB4EaWPvGEa5GeMsX+7rXX7GPEbKkCnwDEscbt0cDAoDOhIu4wMte1waPv77wTFRUVvkM7IrmHGlQ7G/mAn3iF0YHrl2prgZwctV+7CmKpM2kSMGyY7TVjjKsGBgY9FZKLtpLcc3PTbr771TIjR/pJevNml9iLi22vmBNPtPMzALaeffZs+3udsVQsdVavNiXyDAwMuh+LFwODBgH9+wNz5riujjonD0niV6llCgoK0u5O96tlFizwE6+QuuNxoLXVJue33/bq2WVvGRmpLo0MDAwMOhObN9u2vkTCVitXVgIPPhjMWww1NTWIWZbzOT8/H5/61KfS7k7P8JaRDRKA14/9nntMCgEDA4OeDdnN+9Ah4P337f/j8VDe6t+/P356221oqKjATcuWYcuWLZguvAfTgEVp5i1I+UR2xRHcOmYMvvzii66ELTv0X3ONbVQ9dMjWub/8sk34LJrLSOcGBgY9Dvv3AyNHAnv32p9jMTvwKR4Hzj4b+Mc/vDynCGQC4ONEKx4HEVnqk+rR7eT+MwBLmpvti9m82XZfFOqX5mY714IwoorvTOoAAwOD3oDGRlsFE48Da9YAN9zgT5dSUQGMGOGmWJE5btYs99jmZlgrV6ZF7t2vlunXD7j5ZjfgSBC7ULfwpU1mJvDtb/sbMQU5DAwMeiK4inniRNcOyHlt1y7gL3+x/y8qsvmQc5qIhu2gCrrbJfdHAPzL/Pl2xNeOHfbFnX66HY1aUmKTdXm5m8ehqsqfaW3QIDfF7/z5tgHWwMDAoKdi/37bwHrokE3eRC7HzZ9vC7lCWm9qsrPb5ubaqdDXru0dknseYF+YMJy+/747iwE2wQv9k2XZy5fLL/dK6ocPu8d30+RkYGBgkDZKSuygTMDOTSOIHbA5THYgWbHC9RBME8fGz/2ee9zoLMCWwnkumFdesWcuIvvifvc7rzvR1Kn2cZMm6SNWDQwMDHoSBOdx7pswweYwnghRLjKUJrqf3P/nf+zOL19uGxYEeC6Y6mrgrLPs/2tr3SxrBQX28uWuu9yAJeM5Y2Bg0BuwfDlQU2MLsgMG2JqI555ztRVynI6ocZEmul8tM3Cg/U9JCTB3rms8ENkehWGhrc3WRT39tJ1lrazMTqu5apWdu90ELBkYGPQmlJTYAZkvvWR7BebnB3OYqHGRJro/t0x+vuvH+eqrbu52ke2R10kVLpOLF7sVT8QkYGBgYNCbwDPXch7TFO/ofWqZ/Hy/G6SoXKJzA4qS8tfAwMCgJ0PHY5riHY4ePk2ESu6WZc0DcBvsGqp3EdFNimNmAfgvAFkA9hDRLF17eXl5/gyOu3e7Dv39+9u6+Ice8s9gQSl/DQwMDHoydDymS60i9PBWyl6QAEL83C3LygTwBoB6AH8H8CcAFxPR6+yYEgDrAJxNRDssyyojoj2KtigzMxNtbW2wDhzwphIQUV0FBW65Kh7FmpVl7zNpfA0MDHordClUQlKrWJaVlp97mOR+CoAtRLQ9eZJfA2gC8Do75hIADxPRDgBQEbtAPgDrnHPs5QYPtxV5jUU2NTGD8ZBdQfYGBgYGvRHCK2b+fFtaLy+3jaaq/DKAq4tPE2HkPhgADw/dAWCadMwoAFmWZa0GUAjgv4nof1WN5R096uqVOLmL5Yc8g5lMkAYGBscTuMOIsDUCwKhRwMkne0meH5sGwgyqUcI/swBMAdAI4GwA11mWNUp1oLIKE4fs66ly7DcwMDDoreACK4/f2bPHbwptUAAABo9JREFUa1CVa0KngTDJ/e8AeEnuIbCld453YRtRDwE4ZFnWcwBOAvCm3NgHOTlYdtZZwG23YdasWZg1a5a9Qyw/tm61A5iKitwZzGSENDAwOF7AS+sBSnX0mjVrsOapp7w1odNAmEE1BtugehaAnQBeht+gOgbAT2BL7TkAXgLwKSL6m9QWTZ8+HS+++KL/RDzFpYBJ9WtgYNAXwNXRIs/7a6/Z0nxBAayDBzvfoEpERyzLugrA/8F2hfwfInrdsqwlyf0/J6JNlmX9HsCrANoB/EImdgFlFSbAXaoUFdmJxIyO3cDAoK+Aayi4nj072/UeTAPdmvL3/PPPx+OPP+7fKWaum2+2UwuYaksGBgZ9BVwtffCgzYeTJwOlpcCqVbCAnp/yN08XTitmrupqkzPGwMCgb0FI6zt2uJlxhw61c2p1IEK1W8ndyStjqigZGBgY2OBqacCNYO2gQ0n3kvuqVcDf/qbOo2BgYGDQFyFcvl991U4JnJMDXHJJh4Xfbs0Kmff2224VJWM0NTAwMPBK6EOHugZVOdgzRXSv5F5ZCfzxjyYwycDAwEAFoaIpKwN27rTV12miWyX3/M9/3jWaGhgYGBh4IYKcdu50M+WmiW6V3CtHjOjO0xkYGBj0LlxzjV2l6ZVX7M+83mqK6FY/9+46l4GBgUGvhCJav1f4uRsXSAMDA4MAqNwi00T3krtxgTQwMDDQg7tFCseTNNG9ahnAnomMp4yBgYFBJKRbial7JXfjAmlgYGDQLTAGVQMDA4MejN4huRsYGBgYdAsMuRsYGBgchzDkbmBgYHAcwpC7gYGBwXEIQ+4GBgYGxyEMuRsYGBgchwgld8uy5lmWtcmyrDcty/q6Yv8sy7IOWJa1Prl9u2u6amBgYGAQFYHkbllWJoCfAJgH4EQAF1uWNVZx6FoimpzcbuiCfhpIWLNmzbHuwnEDM5adCzOePQNhkvspALYQ0XYiagPwawBNiuNSdrA36BjMC9R5MGPZuTDj2TMQRu6DAbzLPu9IfsdBAE6zLKvVsqzfWZZ1Ymd20MDAwMAgdYRVYoqSL+AvAIYQ0UeWZTUAeAzACR3umYGBgYFB2gjMLWNZ1nQAy4hoXvLzNwC0E9FNAb/ZBmAqEe2TvjeJZQwMDAzSQDq5ZcIk91cAjLIsaxiAnQA+BeBifoBlWQMBvEdEZFnWKbAnjH1yQ+l0zsDA4P+3dz8hNoVhHMe/vwYLUprIn0ZZspzZWEhsKKNEISuTJClrioWtLC2URM1CyoIaGTHKwgYpfyaZpilKYlhQYkWPxX2b7nDPueca99w/8/vU27x3zntvT+88886Zc97nXrN/k7u4R8RPSceBu0APcDkiXks6mo5fBPYCxyT9BH4AB5ocs5mZ1VHaW/6amVl5mlahKmmfpFeSfkkayBmXWyRlIKlX0pikSUn3JNX8tBNJbyW9TMVkT8qOs90VyTVJ59PxF5L6y46xk7jA8f+RdEXStKTxnDGN5WZENKUB66nsmnkADGSM6QGmgHXAQuA5sKFZMXVqA84BJ1L/JHA2Y9wboLfV8bZjK5JrwCAwmvobgUetjrtdW8H53AqMtDrWTmjAZqAfGM843nBuNu3MPSImImKyzrCiRVLz3S5gOPWHgd05Y33jurYiuTYzzxHxGFiWNgzY31zg+B9FxEPgS86QhnOz1W8cVqRIymBlREyn/jSQ9UMN4L6kp5KOlBNaxyiSa7XG9DU5rk7lAsdyNZyb9bZC5pI0BqyqcehURNwq8BK+m5vkzOXp6gcRETk1A5si4oOkFcCYpIl0RmDFc+3PM03naG0ucCxfQ7k5p8U9IrbN5fnAe2Bt1eO1VP4izTt5c5lutKyKiI+SVgOfMl7jQ/r6WdJNKv86e3GvKJJrf47pS9+zv9Wdz4j4VtW/I+mCpN6oUQdjdTWcm2Vdlsm67jZTJCVpEZUiqZGSYuokI8BQ6g9ROQOaRdJiSUtTfwmwHci88z4PFcm1EeAgzFRnf626HGaz1Z1PSSslKfUzCxytkIZzc05n7nkk7QHOA8uB25KeRcQOSWuASxGxMzKKpJoVUwc7C1yXdBh4C+wHqJ5LKpd0bqTfpQXA1Yi415pw209WrlUX5EXEqKRBSVPAd+BQC0Nua0XmExc4FibpGrAFWC7pHXCGyi6kf85NFzGZmXWhVu+WMTOzJvDibmbWhby4m5l1IS/uZmZdyIu7mVkX8uJuZtaFvLibmXUhL+5mZl3oNzr3u+Bf9R2pAAAAAElFTkSuQmCC",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10f44a490>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = np.linspace(-1, 1, 2000)\n",
    "y = np.cos(x) + 0.3*np.random.rand(2000)\n",
    "p = np.polynomial.Chebyshev.fit(x, y, 90)\n",
    "t = np.linspace(-1, 1, 200)\n",
    "plt.plot(x, y, 'r.')  \n",
    "plt.plot(t, p(t), 'k-', lw=3) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "切尔雪夫多项式在插入方面有很多优势。\n",
    "\n",
    "### 1.3.4.2 加载数据文件\n",
    "\n",
    "#### 1.3.4.2.1 文本文件\n",
    "\n",
    "例子: [populations.txt](http://scipy-lectures.github.io/_downloads/populations.txt):\n",
    "\n",
    "```\n",
    "# year  hare    lynx    carrot\n",
    "1900    30e3    4e3     48300\n",
    "1901    47.2e3  6.1e3   48200\n",
    "1902    70.2e3  9.8e3   41500\n",
    "1903    77.4e3  35.2e3  38200\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 222,
   "metadata": {
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[  1900.,  30000.,   4000.,  48300.],\n",
       "       [  1901.,  47200.,   6100.,  48200.],\n",
       "       [  1902.,  70200.,   9800.,  41500.],\n",
       "       [  1903.,  77400.,  35200.,  38200.],\n",
       "       [  1904.,  36300.,  59400.,  40600.],\n",
       "       [  1905.,  20600.,  41700.,  39800.],\n",
       "       [  1906.,  18100.,  19000.,  38600.],\n",
       "       [  1907.,  21400.,  13000.,  42300.],\n",
       "       [  1908.,  22000.,   8300.,  44500.],\n",
       "       [  1909.,  25400.,   9100.,  42100.],\n",
       "       [  1910.,  27100.,   7400.,  46000.],\n",
       "       [  1911.,  40300.,   8000.,  46800.],\n",
       "       [  1912.,  57000.,  12300.,  43800.],\n",
       "       [  1913.,  76600.,  19500.,  40900.],\n",
       "       [  1914.,  52300.,  45700.,  39400.],\n",
       "       [  1915.,  19500.,  51100.,  39000.],\n",
       "       [  1916.,  11200.,  29700.,  36700.],\n",
       "       [  1917.,   7600.,  15800.,  41800.],\n",
       "       [  1918.,  14600.,   9700.,  43300.],\n",
       "       [  1919.,  16200.,  10100.,  41300.],\n",
       "       [  1920.,  24700.,   8600.,  47300.]])"
      ]
     },
     "execution_count": 222,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = np.loadtxt('data/populations.txt')\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 224,
   "metadata": {
    "collapsed": true,
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [],
   "source": [
    "np.savetxt('pop2.txt', data)\n",
    "data2 = np.loadtxt('pop2.txt')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**注**：如果你有一个复杂的文本文件，应该尝试：\n",
    "* `np.genfromtxt`\n",
    "* 使用Python的I/O函数和例如正则式来解析（Python特别适合这个工作）\n",
    "\n",
    "**提示：用IPython在文件系统中航行**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 225,
   "metadata": {
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "u'/Users/cloga/Documents/scipy-lecture-notes_cn'"
      ]
     },
     "execution_count": 225,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pwd      # 显示当前目录"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 227,
   "metadata": {
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/Users/cloga/Documents/scipy-lecture-notes_cn/data\n"
     ]
    }
   ],
   "source": [
    "cd data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 228,
   "metadata": {
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "populations.txt\r\n"
     ]
    }
   ],
   "source": [
    "ls"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 1.3.4.2.2 图像\n",
    "\n",
    "使用Matplotlib："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 233,
   "metadata": {
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((200, 300, 3), dtype('float32'))"
      ]
     },
     "execution_count": 233,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "img = plt.imread('data/elephant.png')\n",
    "img.shape, img.dtype"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 234,
   "metadata": {
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x10fd13f10>"
      ]
     },
     "execution_count": 234,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10fb75510>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(img)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 237,
   "metadata": {
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10fba1750>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.savefig('plot.png')\n",
    "plt.imsave('red_elephant', img[:,:,0], cmap=plt.cm.gray)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "这只保存了一个渠道（RGB）："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 238,
   "metadata": {
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x11040e150>"
      ]
     },
     "execution_count": 238,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10fd39250>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(plt.imread('red_elephant.png')) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "其他包："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 239,
   "metadata": {
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x110bfbfd0>"
      ]
     },
     "execution_count": 239,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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+WhYtpDknLffHhgrctKaqiq9RQ86Xvi1fxvurFSdG3fiC+b6cCgAQuFFQe9spbvzr/3Y3zVm+dKkbb4msd2Mdl/nt2+8fi4kJvq6NreRYDHDDH8vwz21V3pfzdff2uHEAKJPGltt27KQ5pUhTyUki8xuEL9cDgMVE2jlROraOl7pSFkKIBKGiLIQQCUJFWQghEoSKshBCJAgVZSGESBAqykIIkSBmVRIXiPX95ETBjVuGS3UYMUnceW9/Jx0rTPiyPN5tD2B7Yj0HAaASsf8fLfoSrcZ5vE/g2NiwG6/NcFne8ATvldY/7rtrzavm8rEccfj6iw98iOY01PJja2XiADjuy/UAYDLtz6+ulvfo++kDD9CxEpF8TRa5FGxy1JfEIcvfa+w9PfT0s2587elraA51YiROawCQr+HnQ33J74Gcy6RpTrHkf5ZyWX4OlSN9Mfbv9/v3xXKqyb5aW7lbXk8v7zu4rceX351+2tk0p6amxo0zySdDV8pCCJEgVJSFECJBqCgLIUSCUFEWQogEoaIshBAJYlbVF2Ok39e+zcSA5tRIH7BBX7Gxb0cXzVmwahEdS4M8geYPplEu+QYmqYj6IqYOyaT8p/SjQ74iAgBWXnCGGx8Y5H34xnkbQ9Q2+k+nrcJVAplq/zilJ/lT5ky1/2QaAIbH/Pe7aP4JNKev31c+lIlpFAD0kaf6ANDS4pvJbNrcSXNOWrXMjXeSXm0AsGgl76U4PO4raKrz/GM6MuKv+dLFi2nO/PncrCif9497dw83UmJlpCbP+xGOjvNzpVj060Yo82M7XvQ/m/tI30MA6NnLDY7aWv1+gNV1XFFSGPPXrr6BK4I8dKUshBAJQkVZCCEShIqyEEIkCBVlIYRIECrKQgiRIKYtymb2JTPrMbMnDovdaGadZvbo1H+Xz+40hRBibnA0krg7AHwOwJcPiwUAt4QQbokljuz3JT79vTvceOPJjXRb1bW+Vq1/6y9pTrrqdXSsaTGRdYEbrzB5WyUiw4pJ4v7gza9144sjhkQFZmgT6Te2aAGX8QyQPmrpNF+HKy88zY1feN75NOfpZ7bSsVeec44br6nnxj4jY74krnfPbpqzdMXpdGzzU+vd+IKFvN/e/v5BN/7IRj6Hvog+sbGxwY3v7uayzwzpLdi5g0v5tm71P38A0NjU7MaLJV+SCgBDQ75JVirD9aWLFviSMwDo6/PlneUin0NNg187ysR4DABaGvz3CgA1tf72qrO8ZE4UfZnf8AiXl3pMe6UcQvgpAE9IyquNEEKIGXE895TfZ2aPmdntZub7/QkhhDgmZvqNvi8A+NjUzx8H8BkA1x75oru+8OlDP5/xigtx5rkXznB3Qgjx683OHduxa8f2aV83o6IcQjj03UUzuw3Ad7zXveM9fzqTzQshxMuOpcuWY+my5Yd+/8+f/cR93YxuX5hZ+2G/vgXAE+y1Qgghjp5pr5TN7KsALgbQama7AHwEwCVmthYHVBjbALzby61p9k1onr33n9143aKPuXEAqFvsm3osWvkqmvPU/d+iYxe+821uvFKJ/J1K+c82M5FlTIG3afrLm//BjX/mI9fRnNFR/0l3fY3/5B4AvnbTjXTsdz78ETeeS/F5v+N3r3HjzzzzGM1pW7SQjmUyfpuf7t3cMKaq2je7aV+wgOZ0bY0YBS3x1QAVYkIFAGHUVwnk0r6hDgAUC7y9VFWTf+5NFrkSpqHGb/tUIO2tAKAQUQuV+30DnyIxFwMAI+d/KPFzaNPmTXSsbaF/DAeHInMgx6kYaSFlGa5VWL1quRvPxC5ja/x1yBk/fu4+pntBCOFqJ/ylY9qLEEKIo0Lf6BNCiAShoiyEEAlCRVkIIRKEirIQQiQIFWUhhEgQs9ujr+gbcaz8gw+68aZ2LuvqXO/LmdpOP4nmnH/yu+iYwZcFsTgQkcOEyDKmfMkSACw50+/XlstyCc0EkTpNFvbSnMaINOnOj97gxh9++D9pzprTznTjXXv20JyzTl9Dx/b3+UYzmzdx+fslr/kNNz5GjIoAAIHL23Zs2ezGs1X8uuWX2/3329HO5X9Dkd50NWP+WEM9N/apmH++ViLaraqY7LNM+lBGrt/qqn2Xhf4R3hOxjhgIAcA+0kuxZQE3h2IGTB2LT6Q5hXFfXgoAY6O+kVFjE/88Zyv+uZevO7ZrX10pCyFEglBRFkKIBKGiLIQQCUJFWQghEoSKshBCJAgVZSGESBCzKomrPHq/G68r+A5fg428b1dT+wluvOvbX6E5zRf5TnAAUN1CnK3GuSRuss/vlda4fDnNyeR8pzwAwIQvBRvo4/K2Z4u+JG7Fif76AEAmzd9THXG2ao30Cdzb2+3GT1rB55DPcilR8/x5bnx0ZBHNmSz572miwCVx1fV+X0YAGBrx13V+Ez8ntxW2u/HGZt6Ip39wiI6ViNOZRfovDpMedLFek/V5fiwqVf7YWGTekxO+s1xdDe8N2T/gO+wBQC7jn5Pbt/DegiBucP3E9Q4A2lr4OT5JPme7O7fTnMWLO9x4OsN7TXroSlkIIRKEirIQQiQIFWUhhEgQKspCCJEgVJSFECJBzKr6oljrP40feerbfsI8bi5UU+c/bW9edQHNGU7zJ9CjA/4T7bp9u2lOucF/Ej82yf+2/fTOf6Jja9/0Vjf+d99/iOa87/Jz3Pj/eMtFNOcr/8F75/Xv73Pja05ZTXOGh32jqV17+Np1j3OzoomyryDIkz58AJBO+ce2VOHKgoYmrr7Ipv0n5GPD/voAwBVXXO7G+4d5f7zJIlfCLF7kq002PPMszQlp/yNcqfB+dmXwNRre55sBhchnCfB73fX28B6LDRF1T8/efW58XiM3MZpHFC/VtVwB0tLYSscGh311SLnC+/r17PXXLp3ihlIeulIWQogEoaIshBAJQkVZCCEShIqyEEIkiGhRNrMlZnafmT1lZk+a2XVT8WYzW2dmG83sh2bGv1cqhBDiqJnuSrkI4PoQwhoA5wP4IzM7BcANANaFEFYB+NHU70IIIY6TqCQuhNANoHvq5xEzexpAB4ArAFw89bI7AdwPpzAPbH/U3W71ile68UqB98yafMrvGbevn8uF0qe9nY7VneRLvp78j+/TnFNf/043niFmKABw8W9zU6TxAd94aCLlG/QAwG/89h+68f/28c/RnLYlS+lYS73fFzEdkUDla32p2ppTuYyuuZ7/Y6qr1++vtneCnw/VTf68//P736I5C1u4wVFtrS8Ta6jjMqwtu/x5R04HPPLIw3SsPu/Lt1pbeW+6wSHfgKm6hhthTU5yyV6+rs6NL1vGz6FNm7b4+zEuH6ut9/cDAIur/PMrneILO3+BL2/butmfGwDkq7hEcrzgyz7r6+tpzu5O/3yY38rlfx5HfU/ZzJYDOAvAgwDaQggHRYg9ALiVlhBCiKPmqIqymdUBuBvA+0MIv3L5EkIIiF4bCCGEOFqm/UafmWVxoCDfFUK4ZyrcY2YLQwjdZtYOwDUt/db//c6hn1evWoVTVp38AkxZCCF+/ejctRudnfxbrweJFmUzMwC3A9gQQrj1sKF7AVwD4Oap/9/jpOMtb3zT0c5XCCFe1ixe0oHFS54zwn/wwV+6r5vuSvlVAN4O4HEzO/jU7s8A3ATgG2Z2LYDtAK46zvkKIYQAYAduCc/Chs3Cd+7xlQxDvX5bJatpptsb23C/G2849800JwNu/tKd8Z9NNue56qDU6D+9z2X5Gm7//pfoWNvF17jx0bExmpOrJk/oyVNzALjuCt6macFCv4XNyhNX0JztO/y2POOkvRUAnH/OK+iYZfyn9ENDvlERADQ0+OqLZzaspzlbtm2lY+1L/WObqfDHLkuXLXbjm7fw/cxfwJ+J1+R9NYBFVAwNRD2TrePKgt2R+XUsaXfjpTI/x4sTvvnRnm5uQtU6n5sBjREzoJpa3lapo91Xh/QNcEOpsQJXobQSxUSpyFtztS30VTKVsl+HPv7RTyKE8LyDq2/0CSFEglBRFkKIBKGiLIQQCUJFWQghEoSKshBCJAgVZSGESBCz2qMvN7bRjdebL6/ZO/AU3dbk85UjAID+x39Ec9ovfxcdW5n3/x4NPfYAzRnY+GM33rb6dJpz9sWvpWODG/7NjdetfR3NSWdLbrwmO0Jz/uXHm+nYwz+4243f/rmbaM7wiG8UtHOnL3UEgHPP5Wv0za/d68b/14c+RHP++m8/78brq3k/tBRXSKKpxpeQbdniy/8AYLLf7yX36NNccvabb+THtlLxJxgzwekfHHTjNRNcThiTo5XGfYOjQkQS1z/gz6Fj0UKaUx+RcG7p2unG+yIStr79/W78pFXcJKuS4lLDiZK/fvPqfAkiAGTTvqlVVy+XBnroSlkIIRKEirIQQiQIFWUhhEgQKspCCJEgVJSFECJBqCgLIUSCmFVJXHHEl8oMD/mvb563nG6r0tTjxsOJZ9GcvU98h46tOeU8N24dfA4Z8n5aW32nNQDY+cT9dKx2pT/37JjvkgUATz34PTd+6m+8leZglG9v9Vlnu/F3XnUJzWla7ju+hQkuWVp48lo6tmLxiW68YdlpNGfNmRe48Rs+8VmagyyXy4WMP5ad9CWIADA87DuQve2N5/M5pLgL4WTBdwccKHNnskrZn18PkbYBQKbC5W1GZGKlEtcTLu7wz/8APu+eHr8/JQAMTvqucx0tC2hOkaxRf78vlQOAaiKDBICJUV8St63Xl+sBwLxmvz40NPCemx66UhZCiAShoiyEEAlCRVkIIRKEirIQQiQIFWUhhEgQs6q+KBX9J7Y54gxT6H+CbiuV8Z9gZrseozl1TX4PNQDoffZBN25531RkahJuePcj3IBmoswVCdmx/W68aPywrD7ZVyrse2YdzamZx9Uhz/zEz7voDb9Pc7p3+Mdp4SK/RxkAZGtr6Njr3/hGN765cxvN6dztPwWvH+fmL7li5NhW+wY5G9Zzw6uGRl8NkI60vcylYwoQf37lIleAZHNV/n4y/BxixkcAkCd9AosFruYIpBfm+Dg/99NZbgZUk/fPlQUL/f6BANDV1enGczm/pyUA7NjBlRTLTvB7/o0U+LHI5/199Q36n3OGrpSFECJBqCgLIUSCUFEWQogEoaIshBAJIlqUzWyJmd1nZk+Z2ZNmdt1U/EYz6zSzR6f+u/zFma4QQry8mU59UQRwfQhhvZnVAXjYzNYBCABuCSHcMuszFEKIOUS0KIcQugF0T/08YmZPAzior+Kalilqicxoe9czbnxomGuJzrnM77VVx9uXYe9238QIAHZtITK2ev62AluuiMxpbMg3KTkwtsuNn7KaG/Hs3ujPO1XPzVVuu+MrdOx/X3etG9+8kfcqTBfG3fiPtv6M5rz3v/4lHfv6V7/gxtsW8T5ut950oxvfE5E59ZX5PwwX5P2+g317fdMhABgb8WVilcDP43nzmunYyKi/vfGIJC6X82V0xUhPvXyWr8Nwf7cbb6hrpDlGem7W1/Jzsn+Qr+s4MVPa8OTjNKdlgS/HzEb68GUyfB369/lGRpFDi95u32QpH1kHj6O+p2xmywGcBeDgp/V9ZvaYmd1uZk3HtFchhBAuR1WUp25d/CuA94cQRgB8AcAJANYC2APgM7M2QyGEmENM+40+M8sCuBvA/wkh3AMAIYTew8ZvA+AaF3/5nh8c+vnM1Stw5uqVxztfIYT4tWTPnj3o3uPfHjqcaFE2MwNwO4ANIYRbD4u3hxAOfp/1LQDc793+/pW/edQTFkKIlzPt7e1ob3/uq+KPrfctIqa7Un4VgLcDeNzMHp2K/TmAq81sLQ6oMLYBePfxTlgIIcT06oufwb/v7PckOoK075WCQsl/mrzqjJPptiZ3+4Yj/Y0RgxdiiAQAWeKPk6riT0qzFb+VT2AbA5Cp5S1xcnl/7k9u2EBzasw3eenu5WY71739DXRsdK+vABnbz419qnO+KuK8M99MczY9xI195jX7Ri7VXHSAmow/70JxH81ZTFo+AUB50n+8kn7uTt3zqLMGN97UzBUWQwPRK0TqAAAMJElEQVS+ygMA6ohp04lLl9Gcr979dTe+tG0hzWmc30rHyqTtU00d/1yMjvptrJpb+X4aA5dNjQ36Y+WIkoK1dura458nAFBb7R8/ABib8N/TwrZFfHtV/nk8GXgd8tA3+oQQIkGoKAshRIJQURZCiAShoiyEEAlCRVkIIRKEirIQQiSIWe3RVyb96erafMlQc0013Vax6MvR6uv93n0AsGvvs3Rs4QK/f98EMVcBgK7dvsHRaWv4NxU793NdV3nSl8uduIRLqtqWv8aNn1Psojm7nuZ9DEOVr1tc0sb7oa29+PfceG2W92S773vcFClf8de8WOLXDDse8U2tOlYuoTmjA3x+pVBw443zuKyrmkjsrr3mt2nO337+y3SsXPLPh6ZmLh9b3OG/36Z5/HNRW020qgBy9b7xUJHMDQDyRAo2NsxNh/r6h+jYshV+H8rOXbtpTnOT/36rydwAYJQYSgFA20Jf+lZdzaWnB75r93wW1HHJrIeulIUQIkGoKAshRIJQURZCiAShoiyEEAlCRVkIIRLErKovSsTcZMGiNjdeSXHDmPGxSX9glD/FLVT435zuXl+tMNI9QHPOPO98P2eEm8y0NHElxe5u3yxl6YrX05z+HQ+58QJp0QQAuTx/Aj006R+ji970DpozuPlBN/7ziJFSpcif3meJGqASuAHNwg5fPZPJc6VCVVWkNRDZV3mcKzYqKf/jc9apHW4cALI5rnyYLPpqgOZGv9URALQQlUVdI2/f1FTP22xVir4SZmgsco4T46EiMfUBgEyGj23essmNNza20JzhIX9++/bvpzkNDfxc2d/nt3Za0MI/z3miHktnuWLDQ1fKQgiRIFSUhRAiQagoCyFEglBRFkKIBKGiLIQQCUJFWQghEsSsSuKK5m++tsqXovTu4aY6tbkmNz7Rz01FJsaLdKyd9NoqEyMSAKip8yV7O3f305wWcPnRJVe+341vfuy7NGew35f4pKv99QGAy153JR3bv2ebG//Jt++gObmcbw7V0MDf61D/IB1LwzdtykZOz3EiQxwrcAkbUtwcamTEl1Zmqvh7qqv2x4pj/JzMRSRxKPvzq4r0x2tu8WVisbZwluESrcEh//zqaOcGVTD/2q44SWSsAPqG+GemOutLyyYiss9Czj9XJif5+dCxiPcE3d65042Pj/E5wPzPxbZ9W3mOg66UhRAiQagoCyFEglBRFkKIBKGiLIQQCSJalM0sb2YPmtl6M9tgZp+cijeb2Toz22hmPzQz/pRJCCHEURMtyiGEAoBLQwhrAZwB4FIzezWAGwCsCyGsAvCjqd+FEEIcJ9NK4kIIB+2ccgDSAPoBXAHg4qn4nQDuh1OYa2p8CVkLka/0T3AdT2Ozv63OLu5eNb+F9ylraPRleXv2celWd78v4xnv76U5qy/5TTp233f/0Y1f+Xvvozl9u9a78a0P8z58P173r3QMwT8W1RHpVpn8Ke/u4j3U8qT3GwCUir50sRK4pDHAd3WrreWOeCMj3AGwijjppVLcWW5wwD/uNbzNI0JEq7Zk5Wo3Pq+hgeZU1/hyuUyaX2/V13KJ3dCg31dvdIxLy7JZ/xwaHubSwMY6fj709/mfs2zGl5wBgBGXv3yWO08OD/EegqgQ18Aydzvcv993lhsY4jXFY9p7ymaWMrP1AHoA3BdCeApAWwjhYBfRHgC+F6cQQohj4miulCsA1ppZI4AfmNmlR4wHs0gLaCGEEEfNUX+jL4QwaGbfBXAOgB4zWxhC6DazdgDuv+Pu/OYPDv185qkrsHbNyuOdrxBC/Fqyf18f9u+P3DKZIlqUzawVQCmEMGBm1QBeC+CjAO4FcA2Am6f+f4+Xf83v8PupQggxl2hpbUZL63OdSzZt3OK+bror5XYAd5pZCgfuP98VQviRmT0K4Btmdi2A7QCueiEmLYQQc51oUQ4hPAHgbCfeB+Cy6TY+WfFvNf/4AV9BsPakE+i29u3rceOjZX47eynpHQYAm5591o0vO3EVzenp2ufGL3rz1TTnZw/+Ox2rIX3UHrjnLppTafCfQA8WeM+zetLHDQDKOV8NUNzPn7bnmvx1bVvIe571EsMfACiab8Qzv4b3Qxsr+e93cJQrLCbK/D1Z0V+HdIorQIrlghsvF7lyJZfj6pAsMfCqyvPtTYz7BjlLVvDP0sZN3CCnUvKPRS6iYli0yO+X2Ef63AFAqcjNoVIpX3/Q0sp7Ffb0+vVhcTs3GNvby28l1BCFyvAwP4+ZYqmp4di+xqFv9AkhRIJQURZCiAShoiyEEAlCRVkIIRKEirIQQiQIFWUhhEgQs9qjzyZ82cuq5UvceO0S3g+t6xHf7GZJm2/iAgDDQ9ysqKV9oRvfs5/3Dmts8SVs+57dSHMm+7mkqm056ROY4fK2Exb77/fZyQ00p7t7Dx2ryfoSrd99z40059t3/70b7xrkcrQyIoY2pPdaqI6YIhGfoFSOX2dkjJsLlUifOSPGNACQy/vna6w3JMANiQYHfeOajnYuDUyRvnDbt/s95gCgOSKR3LLN79HXvozPYbTkv99Fkb5+tdV+Hz4AaGz0JWQ7d++iOWliUDU0xM/JuohJ1lC/L5eryvN5g0j5CpHegu5mjunVQgghZhUVZSGESBAqykIIkSBUlIUQIkGoKAshRIKYVfVFY+sCN17a77fROeeSd9NtrVzjKwh+/E1u3nP5u/6Yjn3vtr9y47UZviSv+/3r3Pidt/IWhdf8yU10jPGN23nOa899rRs/mcQB4Dt3f5GOnbrqFUc/sSkmJnwTqAsv4B5VGx//BR1bc9Gr3fjP/oO3sTrnrNe48bVn+3EA+NI//xkdqwS/zc9kpH1DqeQbEuVyvG3RxARXZpx26jI3zgx6AGBkzFcYLVvmK5wAoFziLY0WtPlNhPp6O2nOI51dbnznJq4IytdwZU37Ql8ZNTk5SXNqqmvceLHM1S7jEyN0LJ3NufFSpJ3XEGljFVLH1gNEV8pCCJEgVJSFECJBvGhF+ZHHn3mxdpVY7r///pd6CongyQ2bXuopJIKJgn/7Yy4xOsK/KDVXefGK8hMqyirKB3jqaRVlAJicUFEeHVVRPhLdvhBCiAShoiyEEAnCQjg2ucZRb9hsdjYshBAvE0IIz3NSmrWiLIQQ4tjR7QshhEgQKspCCJEgZr0om9nlZvaMmW0ysw/P9v6Sgpl9ycx6zOyJw2LNZrbOzDaa2Q/NzHfzfplgZkvM7D4ze8rMnjSz66bic20d8mb2oJmtN7MNZvbJqficWoeDmFnazB41s+9M/T4n14Exq0XZzNIAPg/gcgCnArjazE6ZzX0miDtw4H0fzg0A1oUQVgH40dTvL2eKAK4PIawBcD6AP5o6/nNqHUIIBQCXhhDWAjgDwKVm9mrMsXU4jPcD2ADg4AOtuboOLrN9pXwegM0hhO0hhCKArwF48yzvMxGEEH4K4EiHkisA3Dn1850ArnxRJ/UiE0LoDiGsn/p5BMDTADowx9YBAEIIB78lkQOQxoFzY86tg5ktBvB6ALcBh3o4zbl1iDHbRbkDwOGNtTqnYnOVthBCz9TPPQB8S66XIWa2HMBZAB7EHFwHM0uZ2XoceL/3hRCewhxcBwCfBfBB/Gqzwrm4DpTZLsrS2xHCAS3inFgfM6sDcDeA94cQfsVrcq6sQwihMnX7YjGAi8zs0iPGX/brYGZvBNAbQngU8DudzoV1mI7ZLsq7ARxu7LoEB66W5yo9ZrYQAMysHYBvLP0ywsyyOFCQ7woh3DMVnnPrcJAQwiCA7wI4B3NvHS4EcIWZbQPwVQCvMbO7MPfWIcpsF+WHAJxkZsvNLAfgrQDuneV9Jpl7AVwz9fM1AO6JvPbXHjMzALcD2BBCuPWwobm2Dq0HFQVmVg3gtQAexRxbhxDCn4cQloQQTgDwuwD+XwjhHZhj6zAds/6NPjP7LQC34sDDjdtDCJ+c1R0mBDP7KoCLAbTiwH2yvwDwbQDfALAUwHYAV4UQBl6qOc42UwqDnwB4HM/9k/TPAPwCc2sdTseBB1ipqf/uCiF8ysyaMYfW4XDM7GIAfxJCuGIur4OHvmYthBAJQt/oE0KIBKGiLIQQCUJFWQghEoSKshBCJAgVZSGESBAqykIIkSBUlIUQIkGoKAshRIL4/2For8nRhdYcAAAAAElFTkSuQmCC",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1109ed050>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from scipy.misc import imsave\n",
    "imsave('tiny_elephant.png', img[::6,::6])\n",
    "plt.imshow(plt.imread('tiny_elephant.png'), interpolation='nearest')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 1.3.4.2.3 Numpy的自有格式\n",
    "\n",
    "Numpy有自有的二进制格式，没有便携性但是I/O高效："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 240,
   "metadata": {
    "collapsed": true,
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [],
   "source": [
    "data = np.ones((3, 3))\n",
    "np.save('pop.npy', data)\n",
    "data3 = np.load('pop.npy')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 1.3.4.2.4 知名的（并且更复杂的）文件格式\n",
    "\n",
    "* HDF5: [h5py](http://code.google.com/p/h5py/), [PyTables](http://pytables.org/)\n",
    "* NetCDF: `scipy.io.netcdf_file`, [netcdf4-python](http://code.google.com/p/netcdf4-python/), ...\n",
    "* Matlab: `scipy.io.loadmat`, `scipy.io.savemat`\n",
    "* MatrixMarket: `scipy.io.mmread`, `scipy.io.mmread`\n",
    "\n",
    "... 如果有人使用，那么就可能有一个对应的Python库。\n",
    "\n",
    "**练习：文本数据文件**\n",
    "\n",
    "写一个Python脚本从[populations.txt](http://scipy-lectures.github.io/_downloads/populations.txt)加载数据，删除前五行和后五行。将这个小数据集存入 `pop2.txt`。\n",
    "\n",
    "**Numpy内部**\n",
    "\n",
    "如果你对Numpy的内部感兴趣, 有一个关于[Advanced Numpy](http://scipy-lectures.github.io/advanced/advanced_numpy/index.html#advanced-numpy)的很好的讨论。\n",
    "\n",
    "## 1.3.5 一些练习\n",
    "\n",
    "### 1.3.5.1 数组操作\n",
    "\n",
    "* 从2D数组（不需要显示的输入）:\n",
    "\n",
    "```\n",
    "[[1,  6, 11],\n",
    " [2,  7, 12],\n",
    " [3,  8, 13],\n",
    " [4,  9, 14],\n",
    " [5, 10, 15]]\n",
    "```\n",
    "\n",
    "并且生成一个第二和第四行的新数组。 \n",
    "\n",
    "* 将数组a的每一列以元素的方式除以数组b (提示: `np.newaxis`): "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 243,
   "metadata": {
    "collapsed": true,
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [],
   "source": [
    "a = np.arange(25).reshape(5, 5)\n",
    "b = np.array([1., 5, 10, 15, 20])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* 难一点的题目：创建10 X 3的随机数数组 （在[0, 1]的范围内）。对于每一行，挑出最接近0.5的数。\n",
    "    * 用 `abs`和 `argsort`找到每一行中最接近的列 `j`。\n",
    "    * 使用象征索引抽取数字。（提示：a[i,j]-数组 `i` 必须包含 `j` 中成分的对应行数）\n",
    "    \n",
    "### 1.3.5.2 图片操作：给Lena加边框\n",
    "\n",
    "让我们从著名的Lena图（http://www.cs.cmu.edu/~chuck/lennapg/） 上开始，用Numpy数组做一些操作。Scipy在 `scipy.lena`函数中提供了这个图的二维数组："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 244,
   "metadata": {
    "collapsed": true,
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [],
   "source": [
    "from scipy import misc\n",
    "lena = misc.lena()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**注**：在旧版的scipy中，你会在 `scipy.lena()`找到lena。\n",
    "\n",
    "这是一些通过我们的操作可以获得图片：使用不同的颜色地图，裁剪图片，改变图片的一部分。\n",
    "\n",
    "![lenas](http://scipy-lectures.github.io/_images/lenas.png)\n",
    "\n",
    "* 让我们用pylab的 `imshow`函数显示这个图片。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 245,
   "metadata": {
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x110f51ad0>"
      ]
     },
     "execution_count": 245,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x110425bd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pylab as plt\n",
    "lena = misc.lena()\n",
    "plt.imshow(lena)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Lena然后以为色彩显示。要将她展示为灰色需要指定一个颜色地图。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 246,
   "metadata": {
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x110fb15d0>"
      ]
     },
     "execution_count": 246,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x110fd0550>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(lena, cmap=plt.cm.gray)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* 用一个更小的图片中心来创建数组：例如，从图像边缘删除30像素。要检查结果，用 `imshow` 显示这个新数组。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 247,
   "metadata": {
    "collapsed": true,
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [],
   "source": [
    "crop_lena = lena[30:-30,30:-30]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* 现在我们为Lena的脸加一个黑色项链形边框。要做到这一点，需要创建一个面具对应于需要变成黑色的像素。这个面具由如下条件定义 `(y-256)**2 + (x-256)**2`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 248,
   "metadata": {
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((512, 1), (1, 512))"
      ]
     },
     "execution_count": 248,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y, x = np.ogrid[0:512,0:512] # x 和 y 像素索引\n",
    "y.shape, x.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 249,
   "metadata": {
    "collapsed": true,
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [],
   "source": [
    "centerx, centery = (256, 256) # 图像中心\n",
    "mask = ((y - centery)**2 + (x - centerx)**2) > 230**2 # 圆形"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "接下来我们为面具对应的图片像素赋值为0。语句非常简单并且直觉化："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 253,
   "metadata": {
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x113d33fd0>"
      ]
     },
     "execution_count": 253,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x113b5c790>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "lena[mask] = 0\n",
    "plt.imshow(lena)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* 接下来：将这个练习的所有命令复制到 `lena_locket.py` 脚本中，并且在IPython中用 `%run lena_locket.py`执行这个脚本，将圆形改为椭圆。\n",
    "\n",
    "### 1.3.5.3 数据统计\n",
    "\n",
    "[populations.txt](http://scipy-lectures.github.io/_downloads/populations.txt)中的数据描述了野兔和猞猁（以及胡萝卜）在加拿大北部过去十年的数量："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 254,
   "metadata": {
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x1135d9510>"
      ]
     },
     "execution_count": 254,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x113d96350>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data = np.loadtxt('data/populations.txt')\n",
    "year, hares, lynxes, carrots = data.T  # 技巧: 列到变量\n",
    "plt.axes([0.2, 0.1, 0.5, 0.8]) \n",
    "plt.plot(year, hares, year, lynxes, year, carrots) \n",
    "plt.legend(('Hare', 'Lynx', 'Carrot'), loc=(1.05, 0.5)) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "根据[populations.txt](http://scipy-lectures.github.io/_downloads/populations.txt)中的数据计算并打印...\n",
    "\n",
    "1. 每个物种在这个时间段内的数量平均数及标准差。\n",
    "2. 每个物种在哪一年数量最多。\n",
    "3. 每一年哪个物种数量最多。（提示：`np.array(['H', 'L', 'C'])`的`argsort` 和象征索引）\n",
    "4. 哪一年数量超过50000。（提示：比较和 `np.any`）\n",
    "5. 每个物种有最少数量的两年。（提示： `argsort`、象征索引）\n",
    "6. 比较（作图）野兔和猞猁总量的变化（看一下 `help(np.gradient)`）。看一下相关（见 `help(np.corrcoef)`）。\n",
    "\n",
    "... 所有都不应该使用for循环。\n",
    "\n",
    "答案：[Python源文件](http://scipy-lectures.github.io/_downloads/2_2_data_statistics.py)\n",
    "\n",
    "### 1.3.5.4 粗略积分估计\n",
    "\n",
    "写一个函数  `f(a, b, c)` 返回$a^b - c$。组成一个24x12x6数组其中包含它值在参数范围[0,1] x [0,1] x [0,1]。\n",
    "\n",
    "接近的3-D积分\n",
    "\n",
    "![math](http://scipy-lectures.github.io/_images/math/7057c0b4df82c2659d776bcdc0eb1c9e16f61f9f.png)\n",
    "\n",
    "在这个体积之上有相同的平均数。准确的结果是![result](http://scipy-lectures.github.io/_images/math/13ff2c8691c09121c0bba41558b2ad22e55e077c.png)... - 你的相对误差是多少？\n",
    "\n",
    "（技巧：使用元素级别的操作和广播。你可以用 `np.ogrid` 获得在 `np.ogrid[0:1:20j]` 范围内的数据点。）\n",
    "\n",
    "**提醒**Python函数："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 255,
   "metadata": {
    "collapsed": true,
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [],
   "source": [
    "def f(a, b, c):\n",
    "    return some_result"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "答案：[Python源文件](http://scipy-lectures.github.io/_downloads/2_3_crude_integration.py)\n",
    "\n",
    "### 1.3.5.5 Mandelbrot集合\n",
    "\n",
    "![mandelbrot](http://scipy-lectures.github.io/_images/2_4_mandelbrot.png)\n",
    "\n",
    "写一个脚本计算Mandelbrot分形。Mandelbrot迭代\n",
    "\n",
    "```python\n",
    "N_max = 50\n",
    "some_threshold = 50\n",
    "c = x + 1j*y\n",
    "for j in xrange(N_max):\n",
    "    z = z**2 + c\n",
    "```\n",
    "\n",
    "点（x, y）属于Mandelbrot集合，如果|c| < some_threshold。\n",
    "\n",
    "作如下计算：\n",
    "\n",
    "* 构建一个网格 c = x + 1j\\*y， 值在范围[-2, 1] x [-1.5, 1.5]\n",
    "* 进行迭代\n",
    "* 构建2-D布尔面具标识输入集合中的点\n",
    "* 用下列方法将结果保存到图片：\n",
    "\n",
    "```python\n",
    "import matplotlib.pyplot as plt\n",
    "plt.imshow(mask.T, extent=[-2, 1, -1.5, 1.5]) \n",
    "plt.gray()\n",
    "plt.savefig('mandelbrot.png')\n",
    "```\n",
    "\n",
    "答案：[Python源文件](http://scipy-lectures.github.io/_downloads/2_4_mandelbrot.py)\n",
    "\n",
    "### 1.3.5.6 马尔科夫链\n",
    "\n",
    "![markov-chain](http://scipy-lectures.github.io/_images/markov-chain.png)\n",
    "\n",
    "马尔可夫链过渡矩阵P以及在状态p的概率分布：\n",
    "1. `0 <= P[i,j] <= 1`：从状态i变化到j的概率\n",
    "2. 过度规则： $p_{new} = P^T p_{old}$\n",
    "3. `all(sum(P, axis=1) == 1)`, `p.sum() == 1`: 正态化\n",
    "\n",
    "写一个脚本产生五种状态，并且：\n",
    "* 构建一个随机矩阵，正态化每一行，以便它是过度矩阵。\n",
    "* 从一个随机（正态化）概率分布`p`开始，并且进行50步=> `p_50`\n",
    "* 计算稳定分布：P.T的特征值为1的特征向量（在数字上最接近1）=> `p_stationary`\n",
    "\n",
    "记住正态化向量 - 我并没有...\n",
    "* 检查一下 `p_50` 和 `p_stationary`是否等于公差1e-5\n",
    "\n",
    "工具箱：`np.random.rand`、 `.dot()`、`np.linalg.eig`、reductions、`abs()`、`argmin`、comparisons、`all`、`np.linalg.norm`等。\n",
    "\n",
    "答案：[Python源文件](http://scipy-lectures.github.io/_downloads/2_5_markov_chain.py)"
   ]
  }
 ],
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